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The Third Version of the Generation, Evaluation & Metrics (GEM) Workshop will be held as part of EMNLP, 📅 December 6, 2023.

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Overview

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Many new NLP applications are cast through the lens of natural language generation. With the advent of these new approaches, many opportunities arise: generation in previously less studied languages, new evaluation paradigms, methods for corpus creation, more efficient architectures, strategies for safe deployments, among many others. At the same time, we can learn from the rich history of NLG research to further improve generation methods.\nThese developments require robust and sound NLG evaluation processes. To that end, the GEM workshop aims to encourage the development of model auditing and human evaluation strategies, and to popularize model evaluations in languages beyond English.

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If you are interested, you can check out last year's workshop websites from ACL 2021 and EMNLP 2022. Our call for this workshop can be found here.

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Schedule

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** This will be filled in a few days**

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All times in local Singapore Time, please use a converter like this one to if you are in a different time zone.\nTo accomodate attendees from as many time zones as possible, we will have a virtual-only part in the evening.

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StartEnd
9:0010:30Opening Remarks + 6 x 12 minutes talk
10:3011:00Coffee Break
11:0012:30Poster Session
12:3014:00Lunch Break
14:0015:307 x 12 minutes talk
15:3016:00Coffee Break
16:0017:30Poster Session II
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Papers

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Here is a list of papers you will be able to see presented at our workshop:

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IDTypeTitleAuthors
223FindingsMacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent SpaceHanxing Ding, Liang Pang, Zihao Wei, Huawei Shen, Xueqi Cheng, Tat-Seng Chua
271FindingsVector-Quantized Prompt Learning for Paraphrase GenerationHaotian Luo, Yixin Liu, Peidong Liu, Xianggen Liu
300FindingsDeltaScore: Story Evaluation with PerturbationsZhuohan Xie, Miao Li, Trevor Cohn, Jey Han Lau
469FindingsShow, Write, and Retrieve: Entity-aware Article Generation and RetrievalZhongping Zhang, Yiwen Gu, Bryan A. Plummer
575FindingsAdversarial Text Generation by Search and LearningGuoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, Honglei Lyu
651FindingsOn Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark StudyPolina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Zhe Liu
731FindingsGROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of EvidenceZhihua Wen, Zhiliang Tian, Wei Wu, Yuxin Yang, Yanqi Shi, Zhen Huang, Dongsheng Li
963FindingsA Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative WritingCarlos Gómez-Rodríguez, Paul Williams
1154FindingsCan Large Language Models Fix Data Annotation Errors? An Empirical Study Using Debatepedia for Query-Focused Text SummarizationMd Tahmid Rahman Laskar, Mizanur Rahman, Israt Jahan, Enamul Hoque, Jimmy Huang
1470FindingsUniform Complexity for Text GenerationJoseph Marvin Imperial, Harish Tayyar Madabushi
1548FindingsUnraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing AssistanceThiemo Wambsganss, Xiaotian Su, Vinitra Swamy, Seyed Parsa Neshaei, Roman Rietsche, Tanja Käser
1562FindingsGeographical Erasure in Language GenerationPola Schwöbel, Jacek Golebiowski, Michele Donini, Cedric Archambeau, Danish Pruthi
1807FindingsMiracle: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute ControlZhenyi Lu, Wei Wei, Xiaoye Qu, Xian-Ling Mao, Dangyang Chen, Jixiong Chen
1834FindingsA Comprehensive Evaluation of Tool-Assisted Generation StrategiesAlon Jacovi, Avi Caciularu, Jonathan Herzig, Roee Aharoni, Bernd Bohnet, Mor Geva
1897FindingsStylized Dialogue Generation with Feature-Guided Knowledge AugmentationJinpeng Li, Zekai Zhang, Xiuying Chen, Dongyan Zhao, Rui Yan
1992FindingsHarnessing the power of LLMs: Evaluating human-AI text co-creation through the lens of news headline generationZijian Ding, Alison Smith-Renner, Wenjuan Zhang, Joel R. Tetreault, Alejandro Jaimes
1993FindingsInfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationRenzhi Wang, Jing Li, Piji Li
2053FindingsThe Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm GenerationTyler Loakman, Aaron Maladry, Chenghua Lin
2490FindingsAsk To The Point: Open-Domain Entity-Centric Question GenerationYuxiang Liu, Jie Huang, Kevin Chang
2493FindingsFrugal Prompting for Dialog ModelsBishal Santra, Sakya Basak, Abhinandan De, Manish Gupta, Pawan Goyal
2716FindingsTowards Informative Open-ended Text Generation with Dynamic Knowledge TriplesZixuan Ren, Yang Zhao, Chengqing Zong
2876FindingsHarnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and ImprovementsYushan Qian, Weinan Zhang, Ting Liu
3010FindingsT5Score: Discriminative Fine-tuning of Generative Evaluation MetricsYiwei Qin, Weizhe Yuan, Graham Neubig, Pengfei Liu
3019FindingsNLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each BenchmarkOscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, Eneko Agirre
3386FindingsNarrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport RewardZhicong Lu, Li Jin, Guangluan Xu, Linmei Hu, Nayu Liu, Xiaoyu Li, Xian Sun, Zequn Zhang, kaiwen wei
3613FindingsGoodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented ModelsLuiza Amador Pozzobon, Beyza Ermis, Patrick Lewis, Sara Hooker
3726FindingsDon’t Add, don’t Miss: Effective Content Preserving Generation from Pre-Selected Text SpansAviv Slobodkin, Avi Caciularu, Eran Hirsch, Ido Dagan
3802FindingsEnsemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMsYoung-Suk Lee, Md Arafat Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Fernandez Astudillo
4841FindingsA Closer Look into Using Large Language Models for Automatic EvaluationCheng-Han Chiang, Hung-yi Lee
4954FindingsPseudointelligence: A Unifying Lens on Language Model EvaluationShikhar Murty, Orr Paradise, Pratyusha Sharma
5156FindingsImproving Pacing in Long-Form Story PlanningYichen Wang, Kevin Yang, Xiaoming Liu, Dan Klein
5166Findings“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference LettersYixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, Nanyun Peng
5563FindingsBridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion ModelsShansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, Lingpeng Kong
5603FindingsExploring Context-Aware Evaluation Metrics for Machine TranslationXinyu Hu, Xunjian Yin, Xiaojun Wan
3Main TrackContextualizing the Limits of Model & Evaluation Dataset Curation on Semantic Similarity Classification TasksDaniel Theron
4Main TrackDialogue Quality and Emotion Annotations for Customer Support ConversationsJohn Mendonca, Patrícia Pereira, Miguel Menezes, Vera Cabarrão, Ana C Farinha, Helena Moniz, Alon Lavie and Isabel Trancoso
7Main TrackFormalizing content creation and evaluation methods for AI-generated social media contentChristian Jensen and Axel Højmark
9Main TrackAutomatic Evaluation of Generative Models with Instruction TuningShuhaib Mehri and Vered Shwartz
11Main TrackFACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationSewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer and Hannaneh Hajishirzi
12Main TrackEffective Proxy for Human Labeling: Ensemble Disagreement Scores in Large Language Models for Industrial NLPWei Du, Laksh Advani, Yashmeet Gambhir, Daniel Perry, Prashant Shiralkar, Zhengzheng Xing and Aaron Colak
14Main TrackAutomatic Reflection Generation for Peer-to-Peer CounselingEmma O'Neil, João Sedoc, Diyi Yang, Haiyi Zhu and Lyle Ungar
16Main TrackOne-Shot and Few-Shot Exemplification ModelingJohn Harvill, Hee Suk Yoon, Eunseop Yoon, Mark Hasegawa-Johnson and Chang Yoo
21Main TrackQAMPARI: A Benchmark for Open-domain Questions with Many AnswersSamuel Amouyal, Tomer Wolfson, Ohad Rubin, Ori Yoran, Jonathan Herzig and Jonathan Berant
23Main TrackUnveiling Safety Vulnerabilities of Large Language ModelsGeorge Kour, Marcel Zalmanovici, Naama Zwerdling, Esther Goldbraich, Ora Nova Fandina, Ateret Anaby Tavor, Orna Raz and Eitan Farchi
24Main TrackAdapting Pre-trained Generative Models for Extractive Question AnsweringPrabir Mallick, Tapas Nayak and Indrajit Bhattacharya
25Main TrackPredicting Question-Answering Performance of Large Language Models through Semantic ConsistencyElla Rabinovich, Samuel Ackerman, Orna Raz, Eitan Farchi and Ateret Anaby Tavor
28Main TrackTowards Effective Long-Form QA with Evidence AugmentationMengxia Yu, Sara Rosenthal, Mihaela Bornea and Avi Sil
30Main TrackHarnessing the Plug-and-Play Controller by PromptingHao Wang and Lei Sha
32Main TrackContext and Literacy Aware Learnable Metric for Text SimplificationJeongwon Kwak, Hyeryun Park, Kyungmo Kim and Jinwook Choi
33Main TrackSynthetic Dialogue Dataset Generation using LLM AgentsYelaman Abdullin, Diego Molla, Bahadorreza Ofoghi, John Yearwood and Qingyang Li
34Main TrackAn Empirical Bayes Framework for Open-Domain Dialogue GenerationJing Yang Lee, Kong Aik Lee and Woon Seng Gan
36Main TrackFlesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language ModelsJoseph Marvin Imperial and Harish Tayyar Madabushi
38Main TrackChatGPT as a Java DecompilerBradley McDanel and Zhanhao Liu
41Main TrackMulti-domain Summarization from Leaderboards to Practice: Re-examining Automatic and Human EvaluationDavid Demeter, Oshin Agarwal, Simon Ben Igeri, Marko Sterbentz, Neil Molino, John Conroy and Ani Nenkova
43Main TrackTargeted Image Data Augmentation Increases Basic Skills Captioning RobustnessValentin Barriere, Felipe del Rio, Andres Carvallo, Carlos Aspillaga, Eugenio Herrera-Berg and Cristian Buc
45Main TrackSeparating form and meaning: Using self-consistency to quantify task understanding across multiple sensesXenia Ohmer, Elia Bruni and Dieuwke Hupkes
46Main TrackText Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual SimilarityJoseph Gatto, Omar Sharif, Parker Seegmiller, Philip Bohlman and Sarah Masud Preum
51Main TrackTo Burst or Not to Burst: Generating and Quantifying Improbable TextKuleen Sasse, Efsun Sarioglu Kayi, Samuel Barham and Edward Staley
52Main TrackAre Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMsXue-Yong Fu, Md Tahmid Rahman Laskar, Cheng Chen and Shashi Bhushan TN
54Main TrackRankAug: Augmented data ranking for text classificationTiasa Singha Roy and Priyam Basu
67Main TrackPost Turing: Mapping the landscape of LLM EvaluationAlexey Tikhonov and Ivan Yamshchikov
56Main TrackElo Uncovered: Robustness and Best Practices in Language Model EvaluationMeriem Boubdir, Edward Kim, Beyza Ermis, Sara Hooker and Marzieh Fadaee
62Main TrackPersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and TraitsEhsan Lotfi, Maxime De Bruyn, Jeska Buhmann and Walter Daelemans
63Main TrackHow well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation ExtractionMohanRaj Chanthran, Lay-Ki Soon, Ong Huey Fang and Bhawani Selvaretnam
57Extended AbstractRobust Tooling and New Resources for Large Language Model Evaluation via CatwalkKyle Richardson, Ian Magnusson, Oyvind Tafjord,Akshita Bhagia, Iz Beltagy, Arman Cohan, Pradeep Dasigi,Jesse Dodge, Dirk Groeneveld, Yuling Gu, Ananya Harsh Jha, Tushar Khot and Nishant Subramani
58Extended AbstractGUMSum: Multi-Genre Data and Evaluation for English Abstractive SummarizationYang Janet Liu and Amir Zeldes
60Extended AbstractNewsMet: A ‘Do It All' dataset of contemporary Metaphors in News headlinesRohan Joseph, Timothy Liu, Aik Beng Ng, Simon See and Sunny Rai
20Extended AbstractOn the State of German (Abstractive) Text SummarizationDennis Aumiller, Jing Fan and Michael Gertz
31Extended AbstractMeasuring misogyny in natural language generation: preliminary results from a case study on two Reddit communitiesAaron Snoswell, Lucinda Nelson, Hao Xue, Flora Salim, Nicolas Suzor and Jean Burgess
35Extended AbstractOn the Learnability of Watermarks for Language ModelsChenchen Gu, Xiang Lisa Li, Percy Liang and Tatsunori Hashimoto
47Extended AbstractDoes Writing with Language Models Reduce Content Diversity?Vishakh Padmakumar and He He
39Extended AbstractGenerative language models exhibit social identity biasesTiancheng Hu, Yara Kyrychenko, Jon Roozenbeek and Nigel Collier
70Industry TrackA Simple yet Efficient Ensemble Approach for AI-generated Text DetectionHarika Abburi, Kalyani Roy, Michael Suesserman, Nirmala Pudota, Balaji Veeramani, Edward Bowen and Sanmitra Bhattacharya
17Industry TrackLeveraging Large Language Models for Enhanced Product Descriptions in eCommerceJianghong Zhou, Bo Liu, Jhalak Nilesh Acharya, Yao Hong, Kuang-chih Lee and Musen Wen
55Industry TrackSeparating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in textIsaac Caswell, Lisa Wang and Isabel Papadimitriou
\n

Organization

\n

Contact:\ngem-benchmark-chairs@googlegroups.com

\n

General Chairs

\n

Khyathi Raghavi Chandu (AI2)

\n

Elizabeth Clark (Google Deepmind)

\n

Kaustubh Dhole (Emory University)

\n

Sebastian Gehrmann (Bloomberg)

\n

João Sedoc (NYU)

\n

Alex Wang (Cohere)

\n

Industry Track Chairs

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Enrico Santus (Bloomberg)

\n

Hooman Sedghamiz (Bayer)

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List of Tasks

The list below links to data statements [1, 2] for each of the datasets that are part of GEM tasks. The template used to produce the initial statements and a guide on how to write them can be found here: [download template] [view guide]. We have released an extended version of this template and an interactive collection tool.

  • conversational_weatherData-to-Text|English
    The purpose of this dataset is to assess how well a model can learn a template-like structure in a very low data setting. The task here is to produce a response to a weather-related query. The reply is further specified through the data attributes and discourse structure in the input. The output contains both the lexicalized text and discourse markers for attributes (e.g., `_ARG_TEMP_ 34`).
  • dartData-to-Text|English
    DART is an English dataset aggregating multiple other data-to-text dataset in a common triple-based format. The new format is completely flat, thus not requiring a model to learn hierarchical structures, while still retaining the full information.
  • e2e_nlgData-to-Text|English
    The E2E NLG dataset is an English benchmark dataset for data-to-text models that verbalize a set of 2-9 key-value attribute pairs in the restaurant domain. The version used for GEM is the cleaned E2E NLG dataset, which filters examples with hallucinations and outputs that don't fully cover all input attributes.
  • mlb_data_to_textData-to-Text|English
    The MLB dataset is an English sport-related data-to-text dataset in the baseball domain. The input is a large table with results of a game and the output is a description of the game.
  • RotoWire_English-GermanData-to-Text|English, German
    This dataset is a data-to-text dataset in the basketball domain. The input are tables in a fixed format with statistics about a game (in English) and the target is a German translation of the originally English description. The translations were done by professional translators with basketball experience. The dataset can be used to evaluate the cross-lingual data-to-text capabilities of a model with complex inputs.
  • sportsett_basketballData-to-Text|English
    The sportsett dataset is an English data-to-text dataset in the basketball domain. The inputs are statistics summarizing an NBA game and the outputs are high-quality descriptions of the game in natural language.
  • surface_realisation_st_2020Data-to-Text|Arabic, Chinese, English, French, Hindi, Indonesian, Japanese, Korean, Portuguese, Russian, Spanish, Castilian
    This dataset was used as part of the multilingual surface realization shared task in which a model gets full or partial universal dependency structures and has to reconstruct the natural language. This dataset support 11 languages.
  • tottoData-to-Text|English
    ToTTo is a high-quality English table-to-text dataset with more than 100,000 examples in which a table from Wikipedia with highlighted cells is paired with a sentence that describes the highlighted cells. All examples in the dataset were post-edited in multiple steps to ensure that the targets are fully faithful to the input information.
  • turku_hockey_data2textData-to-Text|Finnish
    This is a Finnish data-to-text dataset in which the input is structured information about a hockey game and the output a description of the game.
  • viggoData-to-Text|English
    ViGGO is an English data-to-text generation dataset in the video game domain, with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset is relatively small with about 5,000 datasets but very clean, and can thus serve for evaluating transfer learning, low-resource, or few-shot capabilities of neural models.
  • web_nlgData-to-Text|Russian, English
    WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets and short texts that cover about 450 different DBpedia properties. The WebNLG data was originally created to promote the development of RDF verbalisers able to generate short text and to handle micro-planning (i.e., sentence segmentation and ordering, referring expression generation, aggregation); the goal of the task is to generate texts starting from 1 to 7 input triples which have entities in common (so the input is actually a connected Knowledge Graph). The dataset contains about 17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets and 19,000 crowdsourced texts in Russian. A challenging test set section with entities and/or properties that have not been seen at training time is available.
  • CrossWOZDialog Response Generation|Chinese
    CrossWOZ is a Chinese multi-domain task-oriented dialogue dataset . It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation.
  • cs_restaurantsDialog Response Generation|Czech
    The Czech Restaurants dataset is a task oriented dialog dataset in which a model needs to verbalize a response that a service agent could provide which is specified through a series of dialog acts. The dataset originated as a translation of an English dataset to test the generation capabilities of an NLG system on a highly morphologically rich language like Czech.
  • dstc10_track2_task2Dialog Response Generation|En
    The DSTC10 Track2 Task 2 follows the DSTC9 Track1 task, where participants have to implement knowledge-grounded dialog systems. The training dataset is inherited from the DSTC9 challenge and is in the written domain, while the test set is newly collected and consists of noisy ASR transcripts. Hence, the dataset facilitates building models for grounded dialog response generation.
  • RiSAWOZDialog Response Generation|Mandarin Chinese
    RiSAWOZ is a Chinese dialog dataset. It can be used to study various dialogue tasks, such as Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution and Unified Generative Ellipsis and Coreference Resolution.
  • schema_guided_dialogDialog Response Generation|English
    The GEM version of this dataset functions as a response generation dataset. The input specifies dialog acts that a model needs to verbalize. The Schema-Guided Dialog dataset is challenging since it comprises multiple domains from hotel and travel to restaurants, and a wide range of dialog acts. The context of each conversation is provided as well.
  • TaskmasterDialog Response Generation|English
    This is a large task-oriented dialog dataset in which a model has to produce the response. The input contains the context and a structured representation of what the model is supposed to generate. The input is already pre-formatted as string, turning this into a pure text-to-text problem.
  • opusparcusParaphrasing|German, English, Finnish, French, Russian, Swedish
    Opusparcus is a paraphrase corpus for six European languages - German, English, Finnish, French, Russian, and Swedish. The paraphrases consist of subtitles from movies and TV shows.
  • turku_paraphrase_corpusParaphrasing|Finnish
    This is a Finnish paraphrase corpus which consists of pairs of text passages, where a typical passage is about a sentence long. It can be used to either identify or generate paraphrases.
  • FairytaleQAQuestion Generation|English
    The FairytaleQA Dataset is an English-language dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. The Dataset was corrected to support both the tasks of Question Generation and Question Answering.
  • squad_v2Question Generation|English
    SQuAD2.0 is a dataset that tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard. In GEM, we are using this dataset for the question-generation task in which a model should generate squad-like questions from an input text.
  • ARTReasoning|English
    Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.
  • common_genReasoning|English
    CommonGen is an English text generation task to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts, the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. The dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total. Note that the CommonGen test set is private and requires submission to the external leaderboard.
  • BiSECTSimplification|English, German, French, Spanish, Castilian
    This dataset is composed of 1 million complex sentences with the task to split and simplify them while retaining the full meaning. Compared to other simplification corpora, BiSECT requires more significant edits. BiSECT offers splits in English, German, French, and Spanish.
  • cochrane-simplificationSimplification|English
    Cochrane is an English dataset for paragraph-level simplification of medical texts. Cochrane is a database of systematic reviews of clinical questions, many of which have summaries in plain English targeting readers without a university education. The dataset comprises about 4,500 of such pairs.
  • SIMPITIKISimplification|Italian
    SIMPITIKI is an Italian Simplification dataset. Its examples were selected from Italian Wikipedia such that their editing tracking descriptions contain any of the words "Simplified"/"Simplify"/"Simplification".
  • wiki_auto_asset_turkSimplification|English
    WikiAuto is an English simplification dataset that we paired with ASSET and TURK, two very high-quality evaluation datasets, as test sets. The input is an English sentence taken from Wikipedia and the target a simplified sentence. ASSET and TURK contain the same test examples but have references that are simplified in different ways (splitting sentences vs. rewriting and splitting).
  • indonlgSummarization|Indonesian, Javanese, Sundanese
    IndoNLG is a collection of various Indonesian, Javanese, and Sundanese NLG tasks including summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks.
  • mlsumSummarization|German, Spanish, Castilian
    MLSum is a multilingual summarization dataset crawled from different news websites. The GEM version supports the German and Spanish subset alongside specifically collected challenge sets for COVID-related articles to test out-of-domain generalization.
  • OrangeSumSummarization|French
    OrangeSum is a French summarization dataset inspired by XSum. It features two subtasks - abstract generation and title generation. The data was sourced from "Orange Actu" articles between 2011 and 2020.
  • squalitySummarization|English
    SQuALITY (Summarization-format QUestion Answering with Long Input Texts, Yes!) is a summarization dataset that is (1) Abstractive, (2) Long-input - The input document are short stories between 3000--6000 words. (3) Question-focused - Each story is associated with multiple question-summary pairs. (4) Multi-reference - Each question is paired with 4 summaries. (5) High-quality - The summaries are crowdsourced from skilled and trained writers.
  • wiki_cat_sumSummarization|English
    WikiCatSum is an English summarization dataset in three domains - animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs.
  • wiki_linguaSummarization|English, Spanish, Castilian, Portuguese, French, German, Russian, Italian, Indonesian, Dutch, Flemish, Arabic, Chinese, Vietnamese, Thai, Japanese, Korean, Hindi, Czech, Turkish
    Placeholder
  • xlsumSummarization|Amharic, Arabic, Azerbaijani, Bengali, Bangla, Burmese, Chinese (family), English, French, Gujarati, Hausa, Hindi, Igbo, Indonesian, Japanese, Rundi, Korean, Kirghiz, Kyrgyz, Marathi, Nepali (individual language), Oromo, Pushto, Pashto, Persian, Ghanaian Pidgin English, Portuguese, Panjabi, Punjabi, Russian, Scottish Gaelic, Gaelic, Serbian, Romano-Serbian, Sinhala, Sinhalese, Somali, Spanish, Castilian, Swahili (individual language), Kiswahili, Tamil, Telugu, Thai, Tigrinya, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, Yoruba
    XLSum is a highly multilingual summarization dataset supporting 44 language. The data stems from BBC news articles.
  • xsumSummarization|English
    XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it.
  • xwikisSummarization|German, English, French, Czech
    The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation.
  • SciDuetText-to-Slide|English
    This dataset supports the document-to-slide generation task where a model has to generate presentation slide content from the text of a document.
\ No newline at end of file +GEM Tasks

List of Tasks

The list below links to data statements [1, 2] for each of the datasets that are part of GEM tasks. The template used to produce the initial statements and a guide on how to write them can be found here: [download template] [view guide]. We have released an extended version of this template and an interactive collection tool.

  • conversational_weatherData-to-Text|English
    The purpose of this dataset is to assess how well a model can learn a template-like structure in a very low data setting. The task here is to produce a response to a weather-related query. The reply is further specified through the data attributes and discourse structure in the input. The output contains both the lexicalized text and discourse markers for attributes (e.g., `_ARG_TEMP_ 34`).
  • dartData-to-Text|English
    DART is an English dataset aggregating multiple other data-to-text dataset in a common triple-based format. The new format is completely flat, thus not requiring a model to learn hierarchical structures, while still retaining the full information.
  • e2e_nlgData-to-Text|English
    The E2E NLG dataset is an English benchmark dataset for data-to-text models that verbalize a set of 2-9 key-value attribute pairs in the restaurant domain. The version used for GEM is the cleaned E2E NLG dataset, which filters examples with hallucinations and outputs that don't fully cover all input attributes.
  • mlb_data_to_textData-to-Text|English
    The MLB dataset is an English sport-related data-to-text dataset in the baseball domain. The input is a large table with results of a game and the output is a description of the game.
  • RotoWire_English-GermanData-to-Text|English, German
    This dataset is a data-to-text dataset in the basketball domain. The input are tables in a fixed format with statistics about a game (in English) and the target is a German translation of the originally English description. The translations were done by professional translators with basketball experience. The dataset can be used to evaluate the cross-lingual data-to-text capabilities of a model with complex inputs.
  • sportsett_basketballData-to-Text|English
    The sportsett dataset is an English data-to-text dataset in the basketball domain. The inputs are statistics summarizing an NBA game and the outputs are high-quality descriptions of the game in natural language.
  • surface_realisation_st_2020Data-to-Text|Arabic, Chinese, English, French, Hindi, Indonesian, Japanese, Korean, Portuguese, Russian, Spanish, Castilian
    This dataset was used as part of the multilingual surface realization shared task in which a model gets full or partial universal dependency structures and has to reconstruct the natural language. This dataset support 11 languages.
  • tottoData-to-Text|English
    ToTTo is a high-quality English table-to-text dataset with more than 100,000 examples in which a table from Wikipedia with highlighted cells is paired with a sentence that describes the highlighted cells. All examples in the dataset were post-edited in multiple steps to ensure that the targets are fully faithful to the input information.
  • turku_hockey_data2textData-to-Text|Finnish
    This is a Finnish data-to-text dataset in which the input is structured information about a hockey game and the output a description of the game.
  • viggoData-to-Text|English
    ViGGO is an English data-to-text generation dataset in the video game domain, with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset is relatively small with about 5,000 datasets but very clean, and can thus serve for evaluating transfer learning, low-resource, or few-shot capabilities of neural models.
  • web_nlgData-to-Text|Russian, English
    WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets and short texts that cover about 450 different DBpedia properties. The WebNLG data was originally created to promote the development of RDF verbalisers able to generate short text and to handle micro-planning (i.e., sentence segmentation and ordering, referring expression generation, aggregation); the goal of the task is to generate texts starting from 1 to 7 input triples which have entities in common (so the input is actually a connected Knowledge Graph). The dataset contains about 17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets and 19,000 crowdsourced texts in Russian. A challenging test set section with entities and/or properties that have not been seen at training time is available.
  • CrossWOZDialog Response Generation|Chinese
    CrossWOZ is a Chinese multi-domain task-oriented dialogue dataset . It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation.
  • cs_restaurantsDialog Response Generation|Czech
    The Czech Restaurants dataset is a task oriented dialog dataset in which a model needs to verbalize a response that a service agent could provide which is specified through a series of dialog acts. The dataset originated as a translation of an English dataset to test the generation capabilities of an NLG system on a highly morphologically rich language like Czech.
  • dstc10_track2_task2Dialog Response Generation|En
    The DSTC10 Track2 Task 2 follows the DSTC9 Track1 task, where participants have to implement knowledge-grounded dialog systems. The training dataset is inherited from the DSTC9 challenge and is in the written domain, while the test set is newly collected and consists of noisy ASR transcripts. Hence, the dataset facilitates building models for grounded dialog response generation.
  • RiSAWOZDialog Response Generation|Mandarin Chinese
    RiSAWOZ is a Chinese dialog dataset. It can be used to study various dialogue tasks, such as Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution and Unified Generative Ellipsis and Coreference Resolution.
  • schema_guided_dialogDialog Response Generation|English
    The GEM version of this dataset functions as a response generation dataset. The input specifies dialog acts that a model needs to verbalize. The Schema-Guided Dialog dataset is challenging since it comprises multiple domains from hotel and travel to restaurants, and a wide range of dialog acts. The context of each conversation is provided as well.
  • TaskmasterDialog Response Generation|English
    This is a large task-oriented dialog dataset in which a model has to produce the response. The input contains the context and a structured representation of what the model is supposed to generate. The input is already pre-formatted as string, turning this into a pure text-to-text problem.
  • opusparcusParaphrasing|German, English, Finnish, French, Russian, Swedish
    Opusparcus is a paraphrase corpus for six European languages - German, English, Finnish, French, Russian, and Swedish. The paraphrases consist of subtitles from movies and TV shows.
  • turku_paraphrase_corpusParaphrasing|Finnish
    This is a Finnish paraphrase corpus which consists of pairs of text passages, where a typical passage is about a sentence long. It can be used to either identify or generate paraphrases.
  • FairytaleQAQuestion Generation|English
    The FairytaleQA Dataset is an English-language dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. The Dataset was corrected to support both the tasks of Question Generation and Question Answering.
  • squad_v2Question Generation|English
    SQuAD2.0 is a dataset that tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard. In GEM, we are using this dataset for the question-generation task in which a model should generate squad-like questions from an input text.
  • ARTReasoning|English
    Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.
  • common_genReasoning|English
    CommonGen is an English text generation task to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts, the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. The dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total. Note that the CommonGen test set is private and requires submission to the external leaderboard.
  • BiSECTSimplification|English, German, French, Spanish, Castilian
    This dataset is composed of 1 million complex sentences with the task to split and simplify them while retaining the full meaning. Compared to other simplification corpora, BiSECT requires more significant edits. BiSECT offers splits in English, German, French, and Spanish.
  • cochrane-simplificationSimplification|English
    Cochrane is an English dataset for paragraph-level simplification of medical texts. Cochrane is a database of systematic reviews of clinical questions, many of which have summaries in plain English targeting readers without a university education. The dataset comprises about 4,500 of such pairs.
  • SIMPITIKISimplification|Italian
    SIMPITIKI is an Italian Simplification dataset. Its examples were selected from Italian Wikipedia such that their editing tracking descriptions contain any of the words "Simplified"/"Simplify"/"Simplification".
  • wiki_auto_asset_turkSimplification|English
    WikiAuto is an English simplification dataset that we paired with ASSET and TURK, two very high-quality evaluation datasets, as test sets. The input is an English sentence taken from Wikipedia and the target a simplified sentence. ASSET and TURK contain the same test examples but have references that are simplified in different ways (splitting sentences vs. rewriting and splitting).
  • indonlgSummarization|Indonesian, Javanese, Sundanese
    IndoNLG is a collection of various Indonesian, Javanese, and Sundanese NLG tasks including summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks.
  • mlsumSummarization|German, Spanish, Castilian
    MLSum is a multilingual summarization dataset crawled from different news websites. The GEM version supports the German and Spanish subset alongside specifically collected challenge sets for COVID-related articles to test out-of-domain generalization.
  • OrangeSumSummarization|French
    OrangeSum is a French summarization dataset inspired by XSum. It features two subtasks - abstract generation and title generation. The data was sourced from "Orange Actu" articles between 2011 and 2020.
  • squalitySummarization|English
    SQuALITY (Summarization-format QUestion Answering with Long Input Texts, Yes!) is a summarization dataset that is (1) Abstractive, (2) Long-input - The input document are short stories between 3000--6000 words. (3) Question-focused - Each story is associated with multiple question-summary pairs. (4) Multi-reference - Each question is paired with 4 summaries. (5) High-quality - The summaries are crowdsourced from skilled and trained writers.
  • wiki_cat_sumSummarization|English
    WikiCatSum is an English summarization dataset in three domains - animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs.
  • wiki_linguaSummarization|English, Spanish, Castilian, Portuguese, French, German, Russian, Italian, Indonesian, Dutch, Flemish, Arabic, Chinese, Vietnamese, Thai, Japanese, Korean, Hindi, Czech, Turkish
    Placeholder
  • xlsumSummarization|Amharic, Arabic, Azerbaijani, Bengali, Bangla, Burmese, Chinese (family), English, French, Gujarati, Hausa, Hindi, Igbo, Indonesian, Japanese, Rundi, Korean, Kirghiz, Kyrgyz, Marathi, Nepali (individual language), Oromo, Pushto, Pashto, Persian, Ghanaian Pidgin English, Portuguese, Panjabi, Punjabi, Russian, Scottish Gaelic, Gaelic, Serbian, Romano-Serbian, Sinhala, Sinhalese, Somali, Spanish, Castilian, Swahili (individual language), Kiswahili, Tamil, Telugu, Thai, Tigrinya, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, Yoruba
    XLSum is a highly multilingual summarization dataset supporting 44 language. The data stems from BBC news articles.
  • xsumSummarization|English
    XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it.
  • xwikisSummarization|German, English, French, Czech
    The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation.
  • SciDuetText-to-Slide|English
    This dataset supports the document-to-slide generation task where a model has to generate presentation slide content from the text of a document.
\ No newline at end of file diff --git a/data_cards/ART.html b/data_cards/ART.html index 494009d1..93964c81 100644 --- a/data_cards/ART.html +++ b/data_cards/ART.html @@ -1,4 +1,4 @@ -GEM <!-- -->ART
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\ No newline at end of file diff --git a/data_cards/BiSECT.html b/data_cards/BiSECT.html index f13bc419..1f3856c1 100644 --- a/data_cards/BiSECT.html +++ b/data_cards/BiSECT.html @@ -1,4 +1,4 @@ -GEM <!-- -->BiSECT
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\ No newline at end of file diff --git a/data_cards/CrossWOZ.html b/data_cards/CrossWOZ.html index c1d685b7..e97f381b 100644 --- a/data_cards/CrossWOZ.html +++ b/data_cards/CrossWOZ.html @@ -1,4 +1,4 @@ -GEM <!-- -->CrossWOZ
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\ No newline at end of file diff --git a/data_cards/FairytaleQA.html b/data_cards/FairytaleQA.html index c0948b2d..3ac01c42 100644 --- a/data_cards/FairytaleQA.html +++ b/data_cards/FairytaleQA.html @@ -1,4 +1,4 @@ -GEM <!-- -->FairytaleQA
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\ No newline at end of file diff --git a/data_cards/RotoWire_English-German.html b/data_cards/RotoWire_English-German.html index 8378ed3c..9b60e9f7 100644 --- a/data_cards/RotoWire_English-German.html +++ b/data_cards/RotoWire_English-German.html @@ -1,4 +1,4 @@ -GEM <!-- -->RotoWire_English-German
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mlsumSummarization
+GEM <!-- -->mlsum
mlsumSummarization
@@ -1771,4 +1771,4 @@
Any Documented Social Biases?
-
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opusparcusParaphrasing
+GEM <!-- -->opusparcus
opusparcusParaphrasing
@@ -1202,4 +1202,4 @@
Data Splits
-
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\ No newline at end of file diff --git a/data_cards/surface_realisation_st_2020.html b/data_cards/surface_realisation_st_2020.html index e0938208..29540d60 100644 --- a/data_cards/surface_realisation_st_2020.html +++ b/data_cards/surface_realisation_st_2020.html @@ -1,4 +1,4 @@ -GEM <!-- -->surface_realisation_st_2020
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/totto.html b/data_cards/totto.html index 2fd64c2b..e79bc957 100644 --- a/data_cards/totto.html +++ b/data_cards/totto.html @@ -1,4 +1,4 @@ -GEM <!-- -->totto
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/turku_hockey_data2text.html b/data_cards/turku_hockey_data2text.html index 4a9823e7..68ad0bba 100644 --- a/data_cards/turku_hockey_data2text.html +++ b/data_cards/turku_hockey_data2text.html @@ -1,4 +1,4 @@ -GEM <!-- -->turku_hockey_data2text
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/turku_paraphrase_corpus.html b/data_cards/turku_paraphrase_corpus.html index cd5201bd..f77803bf 100644 --- a/data_cards/turku_paraphrase_corpus.html +++ b/data_cards/turku_paraphrase_corpus.html @@ -1,4 +1,4 @@ -GEM <!-- -->turku_paraphrase_corpus
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/viggo.html b/data_cards/viggo.html index a94e5270..fc986f2c 100644 --- a/data_cards/viggo.html +++ b/data_cards/viggo.html @@ -1,4 +1,4 @@ -GEM <!-- -->viggo
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/web_nlg.html b/data_cards/web_nlg.html index 893f6e19..8e06ecf3 100644 --- a/data_cards/web_nlg.html +++ b/data_cards/web_nlg.html @@ -1,4 +1,4 @@ -GEM <!-- -->web_nlg
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/wiki_auto_asset_turk.html b/data_cards/wiki_auto_asset_turk.html index 40f7998c..d86eba0e 100644 --- a/data_cards/wiki_auto_asset_turk.html +++ b/data_cards/wiki_auto_asset_turk.html @@ -1,4 +1,4 @@ -GEM <!-- -->wiki_auto_asset_turk
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/wiki_cat_sum.html b/data_cards/wiki_cat_sum.html index e9736397..7256da7b 100644 --- a/data_cards/wiki_cat_sum.html +++ b/data_cards/wiki_cat_sum.html @@ -1,4 +1,4 @@ -GEM <!-- -->wiki_cat_sum
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/wiki_lingua.html b/data_cards/wiki_lingua.html index b891cac6..ecf567d9 100644 --- a/data_cards/wiki_lingua.html +++ b/data_cards/wiki_lingua.html @@ -1,4 +1,4 @@ -GEM <!-- -->wiki_lingua
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/xlsum.html b/data_cards/xlsum.html index cd64b2bb..a550c2e3 100644 --- a/data_cards/xlsum.html +++ b/data_cards/xlsum.html @@ -1,4 +1,4 @@ -GEM <!-- -->xlsum
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/xsum.html b/data_cards/xsum.html index e664674a..eefa82f5 100644 --- a/data_cards/xsum.html +++ b/data_cards/xsum.html @@ -1,4 +1,4 @@ -GEM <!-- -->xsum
\ No newline at end of file +
\ No newline at end of file diff --git a/data_cards/xwikis.html b/data_cards/xwikis.html index 385583af..fc661d3b 100644 --- a/data_cards/xwikis.html +++ b/data_cards/xwikis.html @@ -1,4 +1,4 @@ -GEM <!-- -->xwikis
\ No newline at end of file +
\ No newline at end of file diff --git a/hackathon.html b/hackathon.html index aabc789f..c08f1e0e 100644 --- a/hackathon.html +++ b/hackathon.html @@ -1,4 +1,4 @@ -GEMv2 Hackathon
Hackathon for GEMv2

GEMv2 Hackathon

+GEMv2 Hackathon
Hackathon for GEMv2

GEMv2 Hackathon

The goal of the hackathon is to work together to add 20+ new tasks into the GEM infrastructure. This page provides answer to the most frequently asked questions.

Important Dates

@@ -27,4 +27,4 @@

Creating challenge sets

We will be using the NL-Augmenter infrastructure to create challenge sets. You can use it to create (1) transformations and (2) filters. Tutorials for multiple possible paths are added on our tutorial page

-
\ No newline at end of file +
\ No newline at end of file diff --git a/index.html b/index.html index e9058569..d9daa991 100644 --- a/index.html +++ b/index.html @@ -1 +1 @@ -GEM

GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics.

GEM aims to:

  • measure NLG progress across many NLG tasks across languages.
  • audit data and models and present results via data cards and model robustness reports.
  • develop standards for evaluation of generated text using both automated and human metrics.

We will regularly update GEM and to encourage more inclusive practices in evaluation by extending existing data or developing datasets for additional languages.

Data Cards

Tutorials

Results

Papers

NL-Augmenter

Workshop

If you have any questions, please join our google group for support.
\ No newline at end of file +GEM

GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics.

GEM aims to:

  • measure NLG progress across many NLG tasks across languages.
  • audit data and models and present results via data cards and model robustness reports.
  • develop standards for evaluation of generated text using both automated and human metrics.

We will regularly update GEM and to encourage more inclusive practices in evaluation by extending existing data or developing datasets for additional languages.

Data Cards

Tutorials

Results

Papers

NL-Augmenter

Workshop

If you have any questions, please join our google group for support.
\ No newline at end of file diff --git a/model_cards.html b/model_cards.html index 454a9965..4632bf48 100644 --- a/model_cards.html +++ b/model_cards.html @@ -1 +1 @@ -GEM Model Cards
GEM Model Cards

The list below links to the work-in-progress data cards for models submitted to GEM. As part of our submission process, we ask participants a series of questions about their models. The current version of our model cards lists the provided answers verbatim. The submission form can be found here. The template used to produce the statements and can be found here: [download template].

\ No newline at end of file +GEM Model Cards
GEM Model Cards

The list below links to the work-in-progress data cards for models submitted to GEM. As part of our submission process, we ask participants a series of questions about their models. The current version of our model cards lists the provided answers verbatim. The submission form can be found here. The template used to produce the statements and can be found here: [download template].

\ No newline at end of file diff --git a/model_cards/FB.html b/model_cards/FB.html index 06c96cd9..34ee0573 100644 --- a/model_cards/FB.html +++ b/model_cards/FB.html @@ -1,4 +1,4 @@ -GEM <!-- -->Self-Training, Acceptability Classifiers and Context-Conditioning
Self-Training, Acceptability Classifiers and Context-ConditioningShared Task 2021

Table of Contents

+GEM <!-- -->Self-Training, Acceptability Classifiers and Context-Conditioning
Self-Training, Acceptability Classifiers and Context-ConditioningShared Task 2021

Table of Contents

  • Model Description
  • Social Impact @@ -137,4 +137,4 @@

    Evaluation Details

    How were your models evaluated? Please include evaluation metric details (including links to code), train/validation/test splits, and model performance on both test and validation sets. If more than one model was trained and evaluated, what was the number of training and evaluation runs, and the variance in scores? If human evaluation was used, please describe the experimental setup.

    We used the BLEU score from the GEM-metrics script to verify that a BART-Base model with no context was slightly better than the T5 baseline on the validation data. The small increase could be explained by the inclusion of the service name in the input. Subsequently, we used a different version of BLEU to compare models as the GEM-metrics were not easy to use in our standard computing environment. We compared BART-Base and BART-Large models with and without templatizing the inputs and with 0, 1 or 5 turns of preceding context, finding that BART-Large with 5 turns of preceding context and templatized inputs worked the best. We then ran 2 rounds of self-training (each round having alternating generation and reconstruction), and observed improvements in the reconstruction match accuracy during this process, even though BLEU scores were either slightly decreased or stayed the same.

    When selecting responses using acceptability classifier, we further analyzed the cases where the selected response was different than the one outputted by the generation model, using a 2-way entailment (to establish paraphrases) via Roberta-large-mnli model between target and selected responses as well as target and original responses. We verified that there were more paraphrases in the former compared to the latter, as well as manually looked at a random sample in each case. We noted that BLEU scores were slightly changed (either increased or decreased) when adding the Acceptability Classifier, but the number of paraphrases (generated response w.r.t target response) were increased.

    -
\ No newline at end of file +
\ No newline at end of file diff --git a/model_cards/NUIG-DSI.html b/model_cards/NUIG-DSI.html index 846f2ea8..9c365a1b 100644 --- a/model_cards/NUIG-DSI.html +++ b/model_cards/NUIG-DSI.html @@ -1,4 +1,4 @@ -GEM <!-- -->NUIG-DSI
NUIG-DSIShared Task 2021

Table of Contents

+GEM <!-- -->NUIG-DSI
NUIG-DSIShared Task 2021

Table of Contents

  • Model Description
  • Social Impact @@ -78,4 +78,4 @@

    Computing Infrastructure Use

    Evaluation Details

    How were your models evaluated? Please include evaluation metric details (including links to code), train/validation/test splits, and model performance on both test and validation sets. If more than one model was trained and evaluated, what was the number of training and evaluation runs, and the variance in scores? If human evaluation was used, please describe the experimental setup.

    We used the GEM metrics evaluation script for automatic evaluation.

    -
\ No newline at end of file +
\ No newline at end of file diff --git a/model_cards/POINTER.html b/model_cards/POINTER.html index 2c6e2181..c855bccf 100644 --- a/model_cards/POINTER.html +++ b/model_cards/POINTER.html @@ -1,4 +1,4 @@ -GEM <!-- -->POINTER
POINTERShared Task 2021

Table of Contents

+GEM <!-- -->POINTER
POINTERShared Task 2021

Table of Contents

  • Model Description
  • Social Impact @@ -99,4 +99,4 @@

    Computing Infrastructure Use

    Evaluation Details

    How were your models evaluated? Please include evaluation metric details (including links to code), train/validation/test splits, and model performance on both test and validation sets. If more than one model was trained and evaluated, what was the number of training and evaluation runs, and the variance in scores? If human evaluation was used, please describe the experimental setup.

    The model performance was evaluated using different metrics for lexical similarity (ROUGE 1/2/L, BLEU, Meteor), semantic similarity (BERTscore, BLEURT) and diversity (MSTTR, Distinct 1/2/3, Unique 1/2/3, Entropy 1/2/3) measures. The file with all described metrics and their results is available at https://github.com/asnota/metrics

    -
\ No newline at end of file +
\ No newline at end of file diff --git a/model_cards/SimpleNER.html b/model_cards/SimpleNER.html index cf8612aa..2a468162 100644 --- a/model_cards/SimpleNER.html +++ b/model_cards/SimpleNER.html @@ -1,4 +1,4 @@ -GEM <!-- -->SimpleNER
SimpleNERShared Task 2021

Table of Contents

+GEM <!-- -->SimpleNER
SimpleNERShared Task 2021

Table of Contents

  • Model Description
  • Social Impact @@ -214,4 +214,4 @@

    Evaluation Details

  • on TurkCorpus-test | BLEU: 67.667 | SARI: 39.695
  • on TurkCorpus-validation | BLEU: 75.672 | SARI: 39.407
-
\ No newline at end of file +
\ No newline at end of file diff --git a/nl_augmenter.html b/nl_augmenter.html index a72b2377..f4f43e1b 100644 --- a/nl_augmenter.html +++ b/nl_augmenter.html @@ -1,4 +1,4 @@ -NL-Augmenter
NL-Augmenter 🦎 → 🐍

The NL-Augmenter is a collaborative effort intended to add transformations of datasets dealing with natural language. Transformations augment text datasets in diverse ways, including: introducing spelling errors, translating to a different language, randomizing names and numbers, paraphrasing, changing the style ... and whatever creative augmentation you contribute. We invite submissions of transformations to this framework by way of a GitHub pull request, through August 31, 2021. All submitters of accepted transformations (and filters) will be included as co-authors on a paper announcing this framework.

+NL-Augmenter
NL-Augmenter 🦎 → 🐍

The NL-Augmenter is a collaborative effort intended to add transformations of datasets dealing with natural language. Transformations augment text datasets in diverse ways, including: introducing spelling errors, translating to a different language, randomizing names and numbers, paraphrasing, changing the style ... and whatever creative augmentation you contribute. We invite submissions of transformations to this framework by way of a GitHub pull request, through August 31, 2021. All submitters of accepted transformations (and filters) will be included as co-authors on a paper announcing this framework.

The framework is hosted as a GitHub repository. The organizers can be contacted at nl-augmenter@googlegroups.com.

Submission timeline

August 31, 2021 Pull request must be opened to be eligible for inclusion in the framework and associated paper

@@ -33,4 +33,4 @@

Organization

  • Jinho D. Choi (Emory University)
  • Abinaya Mahendiran (NEXT Labs, Mphasis)
    • -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/panel.html b/panel.html index 9b0e5619..456be0c9 100644 --- a/panel.html +++ b/panel.html @@ -1 +1 @@ -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/papers.html b/papers.html index cc3694ab..e691a0cb 100644 --- a/papers.html +++ b/papers.html @@ -1 +1 @@ -GEM 💎 Papers
    Our publications.

    We are regularly publishing papers on aspects of GEM that describe findings or resources we find worthwhile to share. Please have a look below:

    GEMv1 OverviewGEM Workshop 2021
    This is our first overview paper, introducing GEM and the initial set of 13 tasks and associated baselines.
    Authors: All GEMv1 participants (see team list)
    This is our second overview paper, expanding GEM to 40 tasks and 51 languages, introducing the automatic evaluation on the HuggingFace Hub.
    Authors: All GEMv2 participants (see team list)
    In this survey paper, we discuss many of the principles underlying GEM and propose a set of best practices to follow for model evaluation. See also the shortened version presented at the MLEval workshop at ICLR 2022.
    Authors: Sebastian Gehrmann, Elizabeth Clark, Thibault Sellam
    Data CardsGEM Workshop 2021
    In "Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards", we describe the approach for data documentation in GEMv1 and the similar approach used by HuggingFace datasets.
    Authors: Angelina McMillan-Major, Salomey Osei, Juan Diego Rodriguez, Pawan Sasanka Ammanamanchi, Sebastian Gehrmann, Yacine Jernite
    Evaluation SuitesNeurIPS 2021
    In the paper "Automatic Construction of Evaluation Suites for Natural Language Generation Datasets", we discuss how to build data collections that test robustness of models and show that they are much more expressive than typical test splits.
    Authors: Simon Mille, Kaustubh Dhole, Saad Mahamood, Laura Perez-Beltrachini, Varun Gangal, Mihir Kale, Emiel van Miltenburg, Sebastian Gehrmann
    This was a collaborative & participatory workshop collecting >117 different ways to transform text and >23 ways to filter out subpopulations of datasets.
    Participants and Authors: Listed in paper (see team list)
    Steering Commitee: Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahmood, Simon Mille, Jascha SohlDickstein, Ashish Srivastava, Samson Tan, Tongshuang Wu and Abinaya Mahendiran
    \ No newline at end of file +GEM 💎 Papers
    Our publications.

    We are regularly publishing papers on aspects of GEM that describe findings or resources we find worthwhile to share. Please have a look below:

    GEMv1 OverviewGEM Workshop 2021
    This is our first overview paper, introducing GEM and the initial set of 13 tasks and associated baselines.
    Authors: All GEMv1 participants (see team list)
    This is our second overview paper, expanding GEM to 40 tasks and 51 languages, introducing the automatic evaluation on the HuggingFace Hub.
    Authors: All GEMv2 participants (see team list)
    In this survey paper, we discuss many of the principles underlying GEM and propose a set of best practices to follow for model evaluation. See also the shortened version presented at the MLEval workshop at ICLR 2022.
    Authors: Sebastian Gehrmann, Elizabeth Clark, Thibault Sellam
    Data CardsGEM Workshop 2021
    In "Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards", we describe the approach for data documentation in GEMv1 and the similar approach used by HuggingFace datasets.
    Authors: Angelina McMillan-Major, Salomey Osei, Juan Diego Rodriguez, Pawan Sasanka Ammanamanchi, Sebastian Gehrmann, Yacine Jernite
    Evaluation SuitesNeurIPS 2021
    In the paper "Automatic Construction of Evaluation Suites for Natural Language Generation Datasets", we discuss how to build data collections that test robustness of models and show that they are much more expressive than typical test splits.
    Authors: Simon Mille, Kaustubh Dhole, Saad Mahamood, Laura Perez-Beltrachini, Varun Gangal, Mihir Kale, Emiel van Miltenburg, Sebastian Gehrmann
    This was a collaborative & participatory workshop collecting >117 different ways to transform text and >23 ways to filter out subpopulations of datasets.
    Participants and Authors: Listed in paper (see team list)
    Steering Commitee: Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahmood, Simon Mille, Jascha SohlDickstein, Ashish Srivastava, Samson Tan, Tongshuang Wu and Abinaya Mahendiran
    \ No newline at end of file diff --git a/resources.html b/resources.html index 3a4bdc60..8544bb97 100644 --- a/resources.html +++ b/resources.html @@ -1 +1 @@ -GEM 💎 Resources
    Using our resources.

    As part of GEM, we are continuously producing resources for the research community. This page provides download links and brief explanations of each.

    Outputs and Scores
    Our growing collection of millions of outputs and automatic scores for 20+ models across all GEM tasks. This resource is to be used for work on model evaluation, to characterize model shortcomings, and to provide baseline outputs for model comparison.
    HuggingFace Loader
    All our datasets can be loaded via this data loader implemented in HuggingFace datasets.
    TFDS Loader
    All our datasets can be loaded via this data loader implemented in TFDS.
    Metrics Repository
    Our package for model evaluation. If you want to compute our full suite of metrics with additional convenience functions like caching and parallelism, simply add your dataset to it and follow the instructions in the README.
    NL-Augmenter
    If you want to run robustness tests on your model and data, NL-Augmenter can help! More information can be found on the dedicated site.
    \ No newline at end of file +GEM 💎 Resources
    Using our resources.

    As part of GEM, we are continuously producing resources for the research community. This page provides download links and brief explanations of each.

    Outputs and Scores
    Our growing collection of millions of outputs and automatic scores for 20+ models across all GEM tasks. This resource is to be used for work on model evaluation, to characterize model shortcomings, and to provide baseline outputs for model comparison.
    HuggingFace Loader
    All our datasets can be loaded via this data loader implemented in HuggingFace datasets.
    TFDS Loader
    All our datasets can be loaded via this data loader implemented in TFDS.
    Metrics Repository
    Our package for model evaluation. If you want to compute our full suite of metrics with additional convenience functions like caching and parallelism, simply add your dataset to it and follow the instructions in the README.
    NL-Augmenter
    If you want to run robustness tests on your model and data, NL-Augmenter can help! More information can be found on the dedicated site.
    \ No newline at end of file diff --git a/results.html b/results.html index d8b826be..94b43a5f 100644 --- a/results.html +++ b/results.html @@ -1 +1 @@ -
    loading ...
    \ No newline at end of file +
    loading ...
    \ No newline at end of file diff --git a/shared_task.html b/shared_task.html index 7cb532a4..8825a4ef 100644 --- a/shared_task.html +++ b/shared_task.html @@ -1,4 +1,4 @@ -GEM Workshop 2021
    Shared Task at the GEM Workshop at ACL 2021

    UPDATE Our submission form is now open! Please account for some extra time to write your model card.

    +GEM Workshop 2021
    Shared Task at the GEM Workshop at ACL 2021

    UPDATE Our submission form is now open! Please account for some extra time to write your model card.

    The GEM workshop features a two-part shared task: Modeling and Evaluation. In the modeling shared task, we ask participants to submit model outputs on the GEM tasks. For the evaluation shared task, participants will have access to outputs from the modeling shared task and computed evaluation metrics. The goal is to draw open-ended insights from the set of data, for example by finding shortcuts models have taken, or by exposing limitations in the metrics.

    Neither of the shared tasks will have a winner or loser and there will be no leaderboard that ranks the results. Instead, we see this as a shared quest toward understanding the limitations and opportunities of current NLG systems. We thus encourage widespread participation for systems of all shapes and sizes.

    To stay up-to-date on announcements, please join our Google Group. The same group may be used for questions and discussions.

    @@ -50,4 +50,4 @@

    Important Dates

    June 25 Notification of Acceptance

    July 9 Camera-ready due

    August 5-6 Workshop Dates

    -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/team.html b/team.html index 81413790..57f520df 100644 --- a/team.html +++ b/team.html @@ -1 +1 @@ -GEMv2 Team 2022
    GEMv2 Team
    GEM is a community-driven effort to improve evaluation of natural language generation. It would not be possible without a large group of collaborators to take on challenging tasks. You can see the contributor list to GEMv1 here.

    This page acts as a directory of our amazing contributors. If you want to join the organization, click here to fill out the sign-up form.

    Sebastian Gehrmann

    Google Research

    Hello World :)
    GEMv1

    Antoine Bosselut

    EPFL

    GEMv1

    Laura Perez-Beltrachini

    University of Edinburgh

    GEMv1

    Samira Shaikh

    UNC Charlotte

    GEMv1

    Wei Xu

    Georgia Tech

    GEMv1

    Esin Durmus

    Stanford University

    GEMv1

    Varun Prashant Gangal

    Carnegie Mellon University

    GEMv1

    Tosin Adewumi

    Luleå University of Technology

    GEMv1

    Pawan Sasanka Ammanamanchi

    IIIT Hyderabad

    GEMv1

    Khyathi Raghavi Chandu

    Carnegie Mellon University

    GEMv1

    Miruna Clinciu

    Edinburgh Centre for Robotics

    GEMv1

    Kaustubh Dhole

    Amelia R&D, New York

    GEMv1

    Ondřej Dušek

    Charles University, Prague

    GEMv1

    Chris Chineye Emezue

    Technical University Munich

    GEMv1

    Cristina Garbacea

    University of Michigan, Ann Arbor

    GEMv1

    Yufang Hou

    IBM Research

    GEMv1

    Harsh Jhamtani

    Carnegie Mellon University

    GEMv1

    Yangfeng Ji

    University of Virginia

    GEMv1

    Shailza Jolly

    Technical University of Kaiserslautern and DFKI, Germany

    GEMv1

    Dhruv Kumar

    University of Waterloo

    GEMv1

    Faisal Ladhak

    Columbia University

    GEMv1

    Aman Madaan

    Carnegie Mellon University

    GEMv1

    Khyati Mahajan

    UNC Charlotte

    GEMv1

    Saad Mahamood

    trivago

    GEMv1

    Angie McMillan-Major

    University of Washington, Huggingface

    GEMv1

    Simon Mille

    Pompeu Fabra University

    GEMv1

    Shashi Narayan

    Google Research

    GEMv1

    Vitaly Nikolaev

    Google Research

    GEMv1

    Rubungo Andre Niyongabo

    Polytechnic University of Catalonia

    GEMv1

    Salomey Osei

    Kwame Nkrumah University of Science and Technology

    GEMv1

    Niranjan Ramesh Rao

    National Institute of Technology Karnataka India

    GEMv1

    Vikas Raunak

    Microsoft

    GEMv1

    Sashank Santhanam

    UNC Charlotte/ JP Morgan

    GEMv1

    João Sedoc

    New York University Stern School of Business

    GEMv1

    Hendrik Strobelt

    IBM Research

    GEMv1

    Nishant Subramani

    Allen institute for AI

    Looking for PhD positions to start Fall 2023
    GEMv1

    Emiel van Miltenburg

    Tilburg University

    GEMv1

    Diyi Yang

    Georgia Tech

    GEMv1

    Yacine Jernite

    Huggingface

    GEMv1

    Akhila Yerukola

    Samsung Research

    GEMv1

    Jiawei Zhou

    Harvard University

    GEMv1

    Nivranshu Pasricha

    National University of Ireland Galway

    Anant Khandelwal

    Amazon

    Sanja Stajner

    Symanto Research

    Yi-Ling Chung

    University of Trento, Fondazione Bruno Kessler

    Jekaterina Novikova

    Winterlight

    Alex Wang

    New York University

    Daniel Deutsch

    University of Pennsylvania

    GEMv1

    Mihir Sanjay Kale

    Google

    GEMv1

    Khyathi Majahan

    UNC Charlotte

    GEMv1

    Pawan Kumar Rajpoot

    Rakuten India

    Nico Daheim

    RWTH Aachen

    Currently applying for PhD positions!

    Thomas Scialom

    Sorbonne university

    Vipul Raheja

    Grammarly

    Mohit Sudhakar

    Google

    Leonardo Ribeiro

    TU Darmstadt

    Steven Feng

    Stanford University

    Michael White

    The Ohio State University / Facebook

    Looking for a postdoc :)

    Alex Fabbri

    Salesforce

    Wout Schellaert

    UPValencia

    Sebastien Montella

    Orange Labs

    Bonaventure Dossou

    Jacobs University Bremen, Mila Quebec AI Institute

    Anna Shvets

    FabLab by Inetum

    SK Mainul Islam

    IIT Kharagpur

    Mohammad Sanad Zaki

    Google

    Richard Plant

    Edinburgh Napier University

    Nick Doiron

    Tufts University / HPE

    Dakuo Wang

    IBM Research

    Jenny Chim

    Queen Mary University of London

    Rabin Banjade

    University of Memphis

    Ronald Cardenas

    University of Edinburgh

    Dongyeop Kang

    University of Minnesota

    Looking for PhD students

    Elizabeth Clark

    Google

    Joshua Maynez

    Google

    Abhik Bhattacharjee

    Bangladesh University of Engineering and Technology

    Tahmid Hasan

    Bangladesh University of Engineering and Technology

    Ananth Rs

    Microsoft

    Aadesh Gupta

    Amelia, Senior RnD

    Eleftheria Briakou

    University of Maryland, College Park

    Joel R Tetreault

    Dataminr

    Roee Aharoni

    Google

    Ashish Shrivastava

    Agara.ai

    Abinaya Mahendiran

    NEXT Labs, Mphasis

    Looking for collaborations

    Jordan Clive

    Imperial College London

    NLG Collaborations Welcome

    Samuel Cahyawijaya

    HKUST

    Qi Zhu

    Tsinghua University

    Bryan Wilie

    The Hong Kong University of Science and Technology

    Mathias Creutz

    University of Helsinki

    Craig Thomson

    University of Aberdeen

    Alexandros Papangelis

    Amazon Alexa AI

    Angelina McMillan-Major

    Hugging Face

    Ashish Upadhyay

    Robert Gordon University

    Leonardo F. R. Ribeiro

    Technical University of Darmstadt

    Moussa Kamal Eddine

    École Polytechnique

    Reno Kriz

    Johns Hopkins University

    Di Jin

    Amazon Alexa AI

    Li Zhang

    University of Pennsylvania

    Filip Ginter

    University of Turku

    Jenna Kanerva

    University of Turku

    Dimitra Gkatzia

    Edinburgh Napier University

    Genta Indra Winata

    The Hong Kong University of Science and Technology

    Ratish Puduppully

    University of Edinburgh

    Sanja Štajner

    Symanto Research

    Mihir Sanjay Kale

    Google Research

    Chandra Bhagavatula

    Allen Institute for AI

    Deyi Xiong

    Tianjin University

    Juraj Juraska

    University of California, Santa Cruz

    Tianhao Shen

    Tianjin University

    Chaobin You

    Tianjin University

    Paul Pu Liang

    Carnegie Mellon University

    Rifat Shahriyar

    Bangladesh University of Engineering and Technology

    Lewis Tunstall

    Hugging Face

    Mahima Pushkarna

    Google Research

    Vivian Tsai

    Google Research

    Yisi Sang

    Syracuse University

    Yixin Liu

    Yale University

    Hiroaki Hayashi

    Salesforce Research

    Dragomir Radev

    Yale University

    Bingsheng Yao

    Rensselaer Polytechnic Institute

    Ying Xu

    University of Michigan

    If you have any questions, please join our google group for support.
    \ No newline at end of file +GEMv2 Team 2022
    GEMv2 Team
    GEM is a community-driven effort to improve evaluation of natural language generation. It would not be possible without a large group of collaborators to take on challenging tasks. You can see the contributor list to GEMv1 here.

    This page acts as a directory of our amazing contributors. If you want to join the organization, click here to fill out the sign-up form.

    Sebastian Gehrmann

    Google Research

    Hello World :)
    GEMv1

    Antoine Bosselut

    EPFL

    GEMv1

    Laura Perez-Beltrachini

    University of Edinburgh

    GEMv1

    Samira Shaikh

    UNC Charlotte

    GEMv1

    Wei Xu

    Georgia Tech

    GEMv1

    Esin Durmus

    Stanford University

    GEMv1

    Varun Prashant Gangal

    Carnegie Mellon University

    GEMv1

    Tosin Adewumi

    Luleå University of Technology

    GEMv1

    Pawan Sasanka Ammanamanchi

    IIIT Hyderabad

    GEMv1

    Khyathi Raghavi Chandu

    Carnegie Mellon University

    GEMv1

    Miruna Clinciu

    Edinburgh Centre for Robotics

    GEMv1

    Kaustubh Dhole

    Amelia R&D, New York

    GEMv1

    Ondřej Dušek

    Charles University, Prague

    GEMv1

    Chris Chineye Emezue

    Technical University Munich

    GEMv1

    Cristina Garbacea

    University of Michigan, Ann Arbor

    GEMv1

    Yufang Hou

    IBM Research

    GEMv1

    Harsh Jhamtani

    Carnegie Mellon University

    GEMv1

    Yangfeng Ji

    University of Virginia

    GEMv1

    Shailza Jolly

    Technical University of Kaiserslautern and DFKI, Germany

    GEMv1

    Dhruv Kumar

    University of Waterloo

    GEMv1

    Faisal Ladhak

    Columbia University

    GEMv1

    Aman Madaan

    Carnegie Mellon University

    GEMv1

    Khyati Mahajan

    UNC Charlotte

    GEMv1

    Saad Mahamood

    trivago

    GEMv1

    Angie McMillan-Major

    University of Washington, Huggingface

    GEMv1

    Simon Mille

    Pompeu Fabra University

    GEMv1

    Shashi Narayan

    Google Research

    GEMv1

    Vitaly Nikolaev

    Google Research

    GEMv1

    Rubungo Andre Niyongabo

    Polytechnic University of Catalonia

    GEMv1

    Salomey Osei

    Kwame Nkrumah University of Science and Technology

    GEMv1

    Niranjan Ramesh Rao

    National Institute of Technology Karnataka India

    GEMv1

    Vikas Raunak

    Microsoft

    GEMv1

    Sashank Santhanam

    UNC Charlotte/ JP Morgan

    GEMv1

    João Sedoc

    New York University Stern School of Business

    GEMv1

    Hendrik Strobelt

    IBM Research

    GEMv1

    Nishant Subramani

    Allen institute for AI

    Looking for PhD positions to start Fall 2023
    GEMv1

    Emiel van Miltenburg

    Tilburg University

    GEMv1

    Diyi Yang

    Georgia Tech

    GEMv1

    Yacine Jernite

    Huggingface

    GEMv1

    Akhila Yerukola

    Samsung Research

    GEMv1

    Jiawei Zhou

    Harvard University

    GEMv1

    Nivranshu Pasricha

    National University of Ireland Galway

    Anant Khandelwal

    Amazon

    Sanja Stajner

    Symanto Research

    Yi-Ling Chung

    University of Trento, Fondazione Bruno Kessler

    Jekaterina Novikova

    Winterlight

    Alex Wang

    New York University

    Daniel Deutsch

    University of Pennsylvania

    GEMv1

    Mihir Sanjay Kale

    Google

    GEMv1

    Khyathi Majahan

    UNC Charlotte

    GEMv1

    Pawan Kumar Rajpoot

    Rakuten India

    Nico Daheim

    RWTH Aachen

    Currently applying for PhD positions!

    Thomas Scialom

    Sorbonne university

    Vipul Raheja

    Grammarly

    Mohit Sudhakar

    Google

    Leonardo Ribeiro

    TU Darmstadt

    Steven Feng

    Stanford University

    Michael White

    The Ohio State University / Facebook

    Looking for a postdoc :)

    Alex Fabbri

    Salesforce

    Wout Schellaert

    UPValencia

    Sebastien Montella

    Orange Labs

    Bonaventure Dossou

    Jacobs University Bremen, Mila Quebec AI Institute

    Anna Shvets

    FabLab by Inetum

    SK Mainul Islam

    IIT Kharagpur

    Mohammad Sanad Zaki

    Google

    Richard Plant

    Edinburgh Napier University

    Nick Doiron

    Tufts University / HPE

    Dakuo Wang

    IBM Research

    Jenny Chim

    Queen Mary University of London

    Rabin Banjade

    University of Memphis

    Ronald Cardenas

    University of Edinburgh

    Dongyeop Kang

    University of Minnesota

    Looking for PhD students

    Elizabeth Clark

    Google

    Joshua Maynez

    Google

    Abhik Bhattacharjee

    Bangladesh University of Engineering and Technology

    Tahmid Hasan

    Bangladesh University of Engineering and Technology

    Ananth Rs

    Microsoft

    Aadesh Gupta

    Amelia, Senior RnD

    Eleftheria Briakou

    University of Maryland, College Park

    Joel R Tetreault

    Dataminr

    Roee Aharoni

    Google

    Ashish Shrivastava

    Agara.ai

    Abinaya Mahendiran

    NEXT Labs, Mphasis

    Looking for collaborations

    Jordan Clive

    Imperial College London

    NLG Collaborations Welcome

    Samuel Cahyawijaya

    HKUST

    Qi Zhu

    Tsinghua University

    Bryan Wilie

    The Hong Kong University of Science and Technology

    Mathias Creutz

    University of Helsinki

    Craig Thomson

    University of Aberdeen

    Alexandros Papangelis

    Amazon Alexa AI

    Angelina McMillan-Major

    Hugging Face

    Ashish Upadhyay

    Robert Gordon University

    Leonardo F. R. Ribeiro

    Technical University of Darmstadt

    Moussa Kamal Eddine

    École Polytechnique

    Reno Kriz

    Johns Hopkins University

    Di Jin

    Amazon Alexa AI

    Li Zhang

    University of Pennsylvania

    Filip Ginter

    University of Turku

    Jenna Kanerva

    University of Turku

    Dimitra Gkatzia

    Edinburgh Napier University

    Genta Indra Winata

    The Hong Kong University of Science and Technology

    Ratish Puduppully

    University of Edinburgh

    Sanja Štajner

    Symanto Research

    Mihir Sanjay Kale

    Google Research

    Chandra Bhagavatula

    Allen Institute for AI

    Deyi Xiong

    Tianjin University

    Juraj Juraska

    University of California, Santa Cruz

    Tianhao Shen

    Tianjin University

    Chaobin You

    Tianjin University

    Paul Pu Liang

    Carnegie Mellon University

    Rifat Shahriyar

    Bangladesh University of Engineering and Technology

    Lewis Tunstall

    Hugging Face

    Mahima Pushkarna

    Google Research

    Vivian Tsai

    Google Research

    Yisi Sang

    Syracuse University

    Yixin Liu

    Yale University

    Hiroaki Hayashi

    Salesforce Research

    Dragomir Radev

    Yale University

    Bingsheng Yao

    Rensselaer Polytechnic Institute

    Ying Xu

    University of Michigan

    If you have any questions, please join our google group for support.
    \ No newline at end of file diff --git a/team/2021.html b/team/2021.html index 09b33e06..f46fc2a9 100644 --- a/team/2021.html +++ b/team/2021.html @@ -1 +1 @@ -GEM Team 2021
    GEMv1 Team
    GEM is a community-driven effort with the goal to improve how progress in natural language generation is measured. It would not be possible without a large group of collaborators to take on challenging tasks.

    This page acts as a directory of our amazing contributors. If you want to join the organization, click here to fill out the sign-up form.

    Sebastian Gehrmann

    Google Research

    Antoine Bosselut

    Stanford University

    Laura Perez-Beltrachini

    University of Edinburgh

    Samira Shaikh

    UNC Charlotte

    Looking for a post-doc

    Wei Xu

    Georgia Tech

    Esin Durmus

    Cornell University

    Varun Prashant Gangal

    Carnegie Mellon University

    Tosin Adewumi

    Luleå University of Technology

    Karmanya Aggarwal

    IIIT Delhi

    Pawan Sasanka Ammanamanchi

    IIIT Hyderabad

    Aremu Anuoluwapo

    University of Lagos

    Khyathi Chandu

    Carnegie Mellon University

    Miruna Clinciu

    Edinburgh Centre for Robotics

    Dipanjan Das

    Google Research

    Kaustubh Dhole

    Amelia R&D, New York

    Looking for Collaborators

    Wanyu Du

    University of Virginia

    Esin Durmus

    Cornell University

    Ondřej Dušek

    Charles University, Prague

    Chris Emezue

    Technical University, Munich

    Looking for PhD positions

    Cristina Garbacea

    University of Michigan, Ann Arbor

    Tatsunori Hashimoto

    Stanford University

    Yufang Hou

    IBM Research

    Harsh Jhamtani

    Carnegie Mellon University

    Yangfeng Ji

    University of Virginia

    Shailza Jolly

    Technical University of Kaiserslautern and DFKI, Germany

    Dhruv Kumar

    University of Waterloo

    Looking for PhD positions

    Faisal Ladhak

    Columbia University

    Aman Madaan

    Carnegie Mellon University

    Mounica Maddela

    Georgia Tech

    Khyati Mahajan

    UNC Charlotte

    Saad Mahamood

    trivago

    Bodhisattwa Prasad Majumder

    University of California, San Diego

    Pedro Henrique Martins

    Instituto de Telecomunicações

    Angie McMillan-Major

    University of Washington, Huggingface

    Simon Mille

    Pompeu Fabra University

    Moin Nadeem

    MIT

    Shashi Narayan

    Google Research

    Vitaly Nikolaev

    Google Research

    Rubungo Andre Niyongabo

    University of Electronic Science and Technology of China

    Salomey Osei

    Kwame Nkrumah University of Science and Technology

    Looking for a PhD position

    Ankur Parikh

    Google Research

    Niranjan Ramesh Rao

    National Institute of Technology Karnataka India

    Vikas Raunak

    Microsoft

    Juan Diego Rodriguez

    Applied Research Laboratories, The University of Texas at Austin

    Sashank Santhanam

    UNC Charlotte

    João Sedoc

    New York University Stern School of Business

    Anastasia Shimorina

    Université de Lorraine

    Marco Antonio Sobrevilla Cabezudo

    University of São Paulo

    Hendrik Strobelt

    IBM Research

    Nishant Subramani

    Intelligent Systems Lab, Intel

    Emiel van Miltenburg

    Tilburg University

    Diyi Yang

    Georgia Tech

    Yacine Yernite

    Huggingface

    Akhila Yerukola

    Samsung Research

    Jiawei Zhou

    Harvard University

    If you have any questions, please join our google group for support.
    \ No newline at end of file +GEM Team 2021
    GEMv1 Team
    GEM is a community-driven effort with the goal to improve how progress in natural language generation is measured. It would not be possible without a large group of collaborators to take on challenging tasks.

    This page acts as a directory of our amazing contributors. If you want to join the organization, click here to fill out the sign-up form.

    Sebastian Gehrmann

    Google Research

    Antoine Bosselut

    Stanford University

    Laura Perez-Beltrachini

    University of Edinburgh

    Samira Shaikh

    UNC Charlotte

    Looking for a post-doc

    Wei Xu

    Georgia Tech

    Esin Durmus

    Cornell University

    Varun Prashant Gangal

    Carnegie Mellon University

    Tosin Adewumi

    Luleå University of Technology

    Karmanya Aggarwal

    IIIT Delhi

    Pawan Sasanka Ammanamanchi

    IIIT Hyderabad

    Aremu Anuoluwapo

    University of Lagos

    Khyathi Chandu

    Carnegie Mellon University

    Miruna Clinciu

    Edinburgh Centre for Robotics

    Dipanjan Das

    Google Research

    Kaustubh Dhole

    Amelia R&D, New York

    Looking for Collaborators

    Wanyu Du

    University of Virginia

    Esin Durmus

    Cornell University

    Ondřej Dušek

    Charles University, Prague

    Chris Emezue

    Technical University, Munich

    Looking for PhD positions

    Cristina Garbacea

    University of Michigan, Ann Arbor

    Tatsunori Hashimoto

    Stanford University

    Yufang Hou

    IBM Research

    Harsh Jhamtani

    Carnegie Mellon University

    Yangfeng Ji

    University of Virginia

    Shailza Jolly

    Technical University of Kaiserslautern and DFKI, Germany

    Dhruv Kumar

    University of Waterloo

    Looking for PhD positions

    Faisal Ladhak

    Columbia University

    Aman Madaan

    Carnegie Mellon University

    Mounica Maddela

    Georgia Tech

    Khyati Mahajan

    UNC Charlotte

    Saad Mahamood

    trivago

    Bodhisattwa Prasad Majumder

    University of California, San Diego

    Pedro Henrique Martins

    Instituto de Telecomunicações

    Angie McMillan-Major

    University of Washington, Huggingface

    Simon Mille

    Pompeu Fabra University

    Moin Nadeem

    MIT

    Shashi Narayan

    Google Research

    Vitaly Nikolaev

    Google Research

    Rubungo Andre Niyongabo

    University of Electronic Science and Technology of China

    Salomey Osei

    Kwame Nkrumah University of Science and Technology

    Looking for a PhD position

    Ankur Parikh

    Google Research

    Niranjan Ramesh Rao

    National Institute of Technology Karnataka India

    Vikas Raunak

    Microsoft

    Juan Diego Rodriguez

    Applied Research Laboratories, The University of Texas at Austin

    Sashank Santhanam

    UNC Charlotte

    João Sedoc

    New York University Stern School of Business

    Anastasia Shimorina

    Université de Lorraine

    Marco Antonio Sobrevilla Cabezudo

    University of São Paulo

    Hendrik Strobelt

    IBM Research

    Nishant Subramani

    Intelligent Systems Lab, Intel

    Emiel van Miltenburg

    Tilburg University

    Diyi Yang

    Georgia Tech

    Yacine Yernite

    Huggingface

    Akhila Yerukola

    Samsung Research

    Jiawei Zhou

    Harvard University

    If you have any questions, please join our google group for support.
    \ No newline at end of file diff --git a/team/join.html b/team/join.html index 7cff805f..aef9a087 100644 --- a/team/join.html +++ b/team/join.html @@ -1 +1 @@ -Help us build GEM 💎
    Sign up to participate in the GEM Organization

    Please use the form below to sign up to help with GEM. We are looking for both junior and senior researchers across many tasks. Even if you are only looking to listen and learn, please sign up.

    The involvement can range from participating in our data hackathon, documenting and improving your own dataset, or helping to write documentation, to organizing the next workshop or shared task. If the form below does not load for you, you can find the form at this URL.

    \ No newline at end of file +Help us build GEM 💎
    Sign up to participate in the GEM Organization

    Please use the form below to sign up to help with GEM. We are looking for both junior and senior researchers across many tasks. Even if you are only looking to listen and learn, please sign up.

    The involvement can range from participating in our data hackathon, documenting and improving your own dataset, or helping to write documentation, to organizing the next workshop or shared task. If the form below does not load for you, you can find the form at this URL.

    \ No newline at end of file diff --git a/turker_faq.html b/turker_faq.html index fd4424f6..5e67fc32 100644 --- a/turker_faq.html +++ b/turker_faq.html @@ -1,4 +1,4 @@ -GEM MTurk Annotation FAQ
    GEM MTurk Annotation FAQ

    If you are working on a mechanical turk HIT and have any questions, please have +GEM MTurk Annotation FAQ

    GEM MTurk Annotation FAQ

    If you are working on a mechanical turk HIT and have any questions, please have a look at the list of frequently asked questions below before contacting us.

    Table of Contents

      @@ -9,4 +9,4 @@

      Question 1

      Answer 1

      Question 2

      Answer 2

      -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/tutorials.html b/tutorials.html index 653c07f2..77e771d3 100644 --- a/tutorials.html +++ b/tutorials.html @@ -1 +1 @@ -GEM Model Cards
    GEM Tutorials

    Here you can find all information to get started using GEM datasets, models, and resources, and how to add new datasets.

    Text Walkthroughs

    Video Guides

    • Creating a filterTransformation
      This walkthrough shows you how to create a filter from scratch using NL-Augmenter.

    Interactive Notebooks

    • From pretrained model to submissionModeling
      This is an interactive version of the introduction tutorial.
    • Creating a filterTransformation
      This notebook shows you how to create a filter from scratch using NL-Augmenter. Please see the accompanying video for in-depth explanations.
    \ No newline at end of file +GEM Model Cards
    GEM Tutorials

    Here you can find all information to get started using GEM datasets, models, and resources, and how to add new datasets.

    Text Walkthroughs

    Video Guides

    • Creating a filterTransformation
      This walkthrough shows you how to create a filter from scratch using NL-Augmenter.

    Interactive Notebooks

    • From pretrained model to submissionModeling
      This is an interactive version of the introduction tutorial.
    • Creating a filterTransformation
      This notebook shows you how to create a filter from scratch using NL-Augmenter. Please see the accompanying video for in-depth explanations.
    \ No newline at end of file diff --git a/tutorials/modeling.html b/tutorials/modeling.html index fc62a0ba..64510983 100644 --- a/tutorials/modeling.html +++ b/tutorials/modeling.html @@ -1,4 +1,4 @@ -GEM <!-- -->From pretrained model to submission
    From pretrained model to submissionModeling

    This tutorial presents a full walk-through on how to get started with GEM, how to load and inspect data, how to finetune a baseline model, and how to generate predictions. +GEM <!-- -->From pretrained model to submission

    From pretrained model to submissionModeling

    This tutorial presents a full walk-through on how to get started with GEM, how to load and inspect data, how to finetune a baseline model, and how to generate predictions. Throughout this tutorial, we will focus on the CommonGen task, but we will note what changes to make to use another of the GEM datasets.

    You can also run this tutorial as a notebook here.

    @@ -331,4 +331,4 @@

    README for more detailed usage information.

    -

    \ No newline at end of file +
    \ No newline at end of file diff --git a/tutorials/new_data_loader.html b/tutorials/new_data_loader.html index 4455f0cf..89258da7 100644 --- a/tutorials/new_data_loader.html +++ b/tutorials/new_data_loader.html @@ -1,4 +1,4 @@ -GEM <!-- -->Adding a data loader
    Adding a data loaderData

    We are using the HuggingFace hub to host all new datasets. All you will have to do is to upload your dataset, along with potential challenge splits, using the steps outlined below.

    +GEM <!-- -->Adding a data loader
    Adding a data loaderData

    We are using the HuggingFace hub to host all new datasets. All you will have to do is to upload your dataset, along with potential challenge splits, using the steps outlined below.

    Table of Contents

    • Setup @@ -82,4 +82,4 @@

      403 Client Error: Forbi

      You may encounter the following when trying to create a dataset: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create - You don't have the rights to create a dataset under this namespace

      This happens when you are not part of the organization or have a typo in the creation command. Ensure that you are (1) logged in, (2) member of the GEM organization, and (3) have typed the --organization GEM command using all upper case letters.

      -

    \ No newline at end of file +
    \ No newline at end of file diff --git a/tutorials/new_nl_augmenter_transformation.html b/tutorials/new_nl_augmenter_transformation.html index 70ecb112..081fbfb4 100644 --- a/tutorials/new_nl_augmenter_transformation.html +++ b/tutorials/new_nl_augmenter_transformation.html @@ -1,4 +1,4 @@ -GEM <!-- -->Using and Adding Transformation to NL-Augmenter
    Using and Adding Transformation to NL-AugmenterTransformation

    This tutorial will demonstrate how to add a new transformation process to the NL-Augmenter library, which researchers can then use to generate new augmented datasets. +GEM <!-- -->Using and Adding Transformation to NL-Augmenter

    Using and Adding Transformation to NL-AugmenterTransformation

    This tutorial will demonstrate how to add a new transformation process to the NL-Augmenter library, which researchers can then use to generate new augmented datasets. We will not cover using filters in this tutorial. If you are interested in using filters, check out the tutorial here.

    Using transformations

    A transformation is represented as a python class. @@ -97,4 +97,4 @@

    Create your pull request

    Conclusion

    And that’s it. NL-Augmenter has an active community of maintainers who will review your request and add your work to the project. In the meantime, others can try out your tranformation by cloning your fork.

    Congratulations on contributing!

    -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/tutorials/writing_a_data_card.html b/tutorials/writing_a_data_card.html index 17c6b5b2..62e0b79a 100644 --- a/tutorials/writing_a_data_card.html +++ b/tutorials/writing_a_data_card.html @@ -1,4 +1,4 @@ -GEM <!-- -->Writing a data card
    Writing a data cardData

    Table of Contents

    +GEM <!-- -->Writing a data card
    \ No newline at end of file +
    \ No newline at end of file diff --git a/workshop.html b/workshop.html index 485c46b4..0de4967f 100644 --- a/workshop.html +++ b/workshop.html @@ -1,52 +1,523 @@ -GEM Workshop 2023
    GEM 💎 Workshop at EMNLP 2023

    The Third Version of the Generation, Evaluation & Metrics (GEM) Workshop will be held as part of EMNLP, December 6-10, 2023.

    -

    Overview

    +GEM Workshop 2023
    GEM 💎 Workshop at EMNLP 2023

    The Third Version of the Generation, Evaluation & Metrics (GEM) Workshop will be held as part of EMNLP, 📅 December 6, 2023.

    +

    Overview

    Many new NLP applications are cast through the lens of natural language generation. With the advent of these new approaches, many opportunities arise: generation in previously less studied languages, new evaluation paradigms, methods for corpus creation, more efficient architectures, strategies for safe deployments, among many others. At the same time, we can learn from the rich history of NLG research to further improve generation methods. These developments require robust and sound NLG evaluation processes. To that end, the GEM workshop aims to encourage the development of model auditing and human evaluation strategies, and to popularize model evaluations in languages beyond English.

    -

    We welcome submissions related, but not limited to, the following topics:

    -
      -
    • 💎 Automatic evaluation of generation systems (example, example, example)
      • -
    • 💎 Creating NLG corpora and challenge sets (example, example, example)
      • -
    • 💎 Critiques of benchmarking efforts and responsibly measuring progress in NLG (example, example)
      • -
    • 💎 Effective and/or efficient NLG methods that can be applied to a wide range of languages and/or scenarios (example, example, example)
      • -
    • 💎 Application and evaluation of generation models interacting with external data and tools (example, example, example)
      • -
    • 💎 Sociotechnical perspectives of employing large language models (example)
      • -
    • 💎 Standardizing human evaluation and making it more robust (example, example, example)
      • -
    -

    We further invite submissions that conduct in-depth analyses of outputs of existing systems, for example through error analyses, by applying new metrics, or by testing the system on new test sets. While we encourage the use of the infrastructure the organizing team has developed as part of the GEM benchmark, its use is not required.

    -

    If you are interested, you can check out last year's workshop websites from ACL 2021 and EMNLP 2022.

    -

    Industrial Track - Unleashing the Power of NLP: Bridging the Gap between Academia and Industry

    -

    GEM 2023 is proud to announce the launch of its Industrial Track, which aims to provide actionable insights to industry professionals and to foster collaborations between academia and industry. This track will address the unique challenges faced by non-academic colleagues, highlighting the differences in evaluation practices between academic and industrial research, and explore the challenges in evaluating generative models with real-world data.

    -

    The Industrial Track invites submissions covering the following topics, including (but not limited to):

    -
      -
    • 💎 Breaking Barriers: Bridging the Gap between Academic and Industrial Research (example)
      • -
    • 💎 From Data Diversity to Model Robustness: Challenges in Evaluating Generative Models with Real-World Data (example)
      • -
    • 💎 Beyond Metrics: Evaluating Generative Models for Real-World Business Impact (example, example, example)
      • -
    -

    How to submit?

    -

    Submissions can take either of the following forms:

    -
      -
    • 💎 Archival Papers Papers describing original and unpublished work can be submitted in a between 4 and 8 page format.
      • -
    • 💎 Non-Archival Abstracts To discuss work already presented or under review at a peer-reviewed venue, we allow the submission of 2-page abstracts.
      • -
    -

    All submissions are allowed unlimited space for references and appendices and should conform to EMNLP 2023 style guidelines. Archival paper submissions must be anonymized while abstract submissions may include author information.

    -

    You can submit directly through SoftConf. Please select the track you are submitting to during the submission.

    -

    We additionally welcome presentations by authors of papers in the Findings of the EMNLP. The selection process is managed centrally by the workshop chairs for the conference and we thus cannot respond to all individual inquiries. However, we will try our best to accomodate your requests.

    -

    Shared Task

    -

    We are organizing a shared task focused on multilingual summarization, including human and automatic evaluation. The Shared Task will be run "Backwards": the workshop will serve as a platform to pre-register your hypotheses. More info on how to participate to come!

    -

    Important Dates

    -

    Note: For any questions, please email gem-benchmark-chairs@googlegroups.com.

    -

    Paper Submission Dates

    -
      -
    • 📅 8 September 2023: Workshop paper submission deadline
      • -
    • 📅 20 October 2023: Workshop paper notification deadline
      • -
    • 📅 3 November 2023: Workshop paper camera ready deadline
      • -
    -

    Note The website showed wrong dates for notication and CR deadlines. Apologies for any inconvenience.

    -

    Workshop Dates

    -
      -
    • 📅 6 December 2022: Workshop
      • -
    -

    Organization

    +

    If you are interested, you can check out last year's workshop websites from ACL 2021 and EMNLP 2022. Our call for this workshop can be found here.

    +

    Schedule

    +

    ** This will be filled in a few days**

    +

    All times in local Singapore Time, please use a converter like this one to if you are in a different time zone. +To accomodate attendees from as many time zones as possible, we will have a virtual-only part in the evening.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    StartEnd
    9:0010:30Opening Remarks + 6 x 12 minutes talk
    10:3011:00Coffee Break
    11:0012:30Poster Session
    12:3014:00Lunch Break
    14:0015:307 x 12 minutes talk
    15:3016:00Coffee Break
    16:0017:30Poster Session II
    +

    Papers

    +

    Here is a list of papers you will be able to see presented at our workshop:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    IDTypeTitleAuthors
    223FindingsMacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent SpaceHanxing Ding, Liang Pang, Zihao Wei, Huawei Shen, Xueqi Cheng, Tat-Seng Chua
    271FindingsVector-Quantized Prompt Learning for Paraphrase GenerationHaotian Luo, Yixin Liu, Peidong Liu, Xianggen Liu
    300FindingsDeltaScore: Story Evaluation with PerturbationsZhuohan Xie, Miao Li, Trevor Cohn, Jey Han Lau
    469FindingsShow, Write, and Retrieve: Entity-aware Article Generation and RetrievalZhongping Zhang, Yiwen Gu, Bryan A. Plummer
    575FindingsAdversarial Text Generation by Search and LearningGuoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, Honglei Lyu
    651FindingsOn Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark StudyPolina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Zhe Liu
    731FindingsGROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of EvidenceZhihua Wen, Zhiliang Tian, Wei Wu, Yuxin Yang, Yanqi Shi, Zhen Huang, Dongsheng Li
    963FindingsA Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative WritingCarlos Gómez-Rodríguez, Paul Williams
    1154FindingsCan Large Language Models Fix Data Annotation Errors? An Empirical Study Using Debatepedia for Query-Focused Text SummarizationMd Tahmid Rahman Laskar, Mizanur Rahman, Israt Jahan, Enamul Hoque, Jimmy Huang
    1470FindingsUniform Complexity for Text GenerationJoseph Marvin Imperial, Harish Tayyar Madabushi
    1548FindingsUnraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing AssistanceThiemo Wambsganss, Xiaotian Su, Vinitra Swamy, Seyed Parsa Neshaei, Roman Rietsche, Tanja Käser
    1562FindingsGeographical Erasure in Language GenerationPola Schwöbel, Jacek Golebiowski, Michele Donini, Cedric Archambeau, Danish Pruthi
    1807FindingsMiracle: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute ControlZhenyi Lu, Wei Wei, Xiaoye Qu, Xian-Ling Mao, Dangyang Chen, Jixiong Chen
    1834FindingsA Comprehensive Evaluation of Tool-Assisted Generation StrategiesAlon Jacovi, Avi Caciularu, Jonathan Herzig, Roee Aharoni, Bernd Bohnet, Mor Geva
    1897FindingsStylized Dialogue Generation with Feature-Guided Knowledge AugmentationJinpeng Li, Zekai Zhang, Xiuying Chen, Dongyan Zhao, Rui Yan
    1992FindingsHarnessing the power of LLMs: Evaluating human-AI text co-creation through the lens of news headline generationZijian Ding, Alison Smith-Renner, Wenjuan Zhang, Joel R. Tetreault, Alejandro Jaimes
    1993FindingsInfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationRenzhi Wang, Jing Li, Piji Li
    2053FindingsThe Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm GenerationTyler Loakman, Aaron Maladry, Chenghua Lin
    2490FindingsAsk To The Point: Open-Domain Entity-Centric Question GenerationYuxiang Liu, Jie Huang, Kevin Chang
    2493FindingsFrugal Prompting for Dialog ModelsBishal Santra, Sakya Basak, Abhinandan De, Manish Gupta, Pawan Goyal
    2716FindingsTowards Informative Open-ended Text Generation with Dynamic Knowledge TriplesZixuan Ren, Yang Zhao, Chengqing Zong
    2876FindingsHarnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and ImprovementsYushan Qian, Weinan Zhang, Ting Liu
    3010FindingsT5Score: Discriminative Fine-tuning of Generative Evaluation MetricsYiwei Qin, Weizhe Yuan, Graham Neubig, Pengfei Liu
    3019FindingsNLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each BenchmarkOscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, Eneko Agirre
    3386FindingsNarrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport RewardZhicong Lu, Li Jin, Guangluan Xu, Linmei Hu, Nayu Liu, Xiaoyu Li, Xian Sun, Zequn Zhang, kaiwen wei
    3613FindingsGoodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented ModelsLuiza Amador Pozzobon, Beyza Ermis, Patrick Lewis, Sara Hooker
    3726FindingsDon’t Add, don’t Miss: Effective Content Preserving Generation from Pre-Selected Text SpansAviv Slobodkin, Avi Caciularu, Eran Hirsch, Ido Dagan
    3802FindingsEnsemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMsYoung-Suk Lee, Md Arafat Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Fernandez Astudillo
    4841FindingsA Closer Look into Using Large Language Models for Automatic EvaluationCheng-Han Chiang, Hung-yi Lee
    4954FindingsPseudointelligence: A Unifying Lens on Language Model EvaluationShikhar Murty, Orr Paradise, Pratyusha Sharma
    5156FindingsImproving Pacing in Long-Form Story PlanningYichen Wang, Kevin Yang, Xiaoming Liu, Dan Klein
    5166Findings“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference LettersYixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, Nanyun Peng
    5563FindingsBridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion ModelsShansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, Lingpeng Kong
    5603FindingsExploring Context-Aware Evaluation Metrics for Machine TranslationXinyu Hu, Xunjian Yin, Xiaojun Wan
    3Main TrackContextualizing the Limits of Model & Evaluation Dataset Curation on Semantic Similarity Classification TasksDaniel Theron
    4Main TrackDialogue Quality and Emotion Annotations for Customer Support ConversationsJohn Mendonca, Patrícia Pereira, Miguel Menezes, Vera Cabarrão, Ana C Farinha, Helena Moniz, Alon Lavie and Isabel Trancoso
    7Main TrackFormalizing content creation and evaluation methods for AI-generated social media contentChristian Jensen and Axel Højmark
    9Main TrackAutomatic Evaluation of Generative Models with Instruction TuningShuhaib Mehri and Vered Shwartz
    11Main TrackFACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationSewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer and Hannaneh Hajishirzi
    12Main TrackEffective Proxy for Human Labeling: Ensemble Disagreement Scores in Large Language Models for Industrial NLPWei Du, Laksh Advani, Yashmeet Gambhir, Daniel Perry, Prashant Shiralkar, Zhengzheng Xing and Aaron Colak
    14Main TrackAutomatic Reflection Generation for Peer-to-Peer CounselingEmma O'Neil, João Sedoc, Diyi Yang, Haiyi Zhu and Lyle Ungar
    16Main TrackOne-Shot and Few-Shot Exemplification ModelingJohn Harvill, Hee Suk Yoon, Eunseop Yoon, Mark Hasegawa-Johnson and Chang Yoo
    21Main TrackQAMPARI: A Benchmark for Open-domain Questions with Many AnswersSamuel Amouyal, Tomer Wolfson, Ohad Rubin, Ori Yoran, Jonathan Herzig and Jonathan Berant
    23Main TrackUnveiling Safety Vulnerabilities of Large Language ModelsGeorge Kour, Marcel Zalmanovici, Naama Zwerdling, Esther Goldbraich, Ora Nova Fandina, Ateret Anaby Tavor, Orna Raz and Eitan Farchi
    24Main TrackAdapting Pre-trained Generative Models for Extractive Question AnsweringPrabir Mallick, Tapas Nayak and Indrajit Bhattacharya
    25Main TrackPredicting Question-Answering Performance of Large Language Models through Semantic ConsistencyElla Rabinovich, Samuel Ackerman, Orna Raz, Eitan Farchi and Ateret Anaby Tavor
    28Main TrackTowards Effective Long-Form QA with Evidence AugmentationMengxia Yu, Sara Rosenthal, Mihaela Bornea and Avi Sil
    30Main TrackHarnessing the Plug-and-Play Controller by PromptingHao Wang and Lei Sha
    32Main TrackContext and Literacy Aware Learnable Metric for Text SimplificationJeongwon Kwak, Hyeryun Park, Kyungmo Kim and Jinwook Choi
    33Main TrackSynthetic Dialogue Dataset Generation using LLM AgentsYelaman Abdullin, Diego Molla, Bahadorreza Ofoghi, John Yearwood and Qingyang Li
    34Main TrackAn Empirical Bayes Framework for Open-Domain Dialogue GenerationJing Yang Lee, Kong Aik Lee and Woon Seng Gan
    36Main TrackFlesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language ModelsJoseph Marvin Imperial and Harish Tayyar Madabushi
    38Main TrackChatGPT as a Java DecompilerBradley McDanel and Zhanhao Liu
    41Main TrackMulti-domain Summarization from Leaderboards to Practice: Re-examining Automatic and Human EvaluationDavid Demeter, Oshin Agarwal, Simon Ben Igeri, Marko Sterbentz, Neil Molino, John Conroy and Ani Nenkova
    43Main TrackTargeted Image Data Augmentation Increases Basic Skills Captioning RobustnessValentin Barriere, Felipe del Rio, Andres Carvallo, Carlos Aspillaga, Eugenio Herrera-Berg and Cristian Buc
    45Main TrackSeparating form and meaning: Using self-consistency to quantify task understanding across multiple sensesXenia Ohmer, Elia Bruni and Dieuwke Hupkes
    46Main TrackText Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual SimilarityJoseph Gatto, Omar Sharif, Parker Seegmiller, Philip Bohlman and Sarah Masud Preum
    51Main TrackTo Burst or Not to Burst: Generating and Quantifying Improbable TextKuleen Sasse, Efsun Sarioglu Kayi, Samuel Barham and Edward Staley
    52Main TrackAre Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMsXue-Yong Fu, Md Tahmid Rahman Laskar, Cheng Chen and Shashi Bhushan TN
    54Main TrackRankAug: Augmented data ranking for text classificationTiasa Singha Roy and Priyam Basu
    67Main TrackPost Turing: Mapping the landscape of LLM EvaluationAlexey Tikhonov and Ivan Yamshchikov
    56Main TrackElo Uncovered: Robustness and Best Practices in Language Model EvaluationMeriem Boubdir, Edward Kim, Beyza Ermis, Sara Hooker and Marzieh Fadaee
    62Main TrackPersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and TraitsEhsan Lotfi, Maxime De Bruyn, Jeska Buhmann and Walter Daelemans
    63Main TrackHow well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation ExtractionMohanRaj Chanthran, Lay-Ki Soon, Ong Huey Fang and Bhawani Selvaretnam
    57Extended AbstractRobust Tooling and New Resources for Large Language Model Evaluation via CatwalkKyle Richardson, Ian Magnusson, Oyvind Tafjord,Akshita Bhagia, Iz Beltagy, Arman Cohan, Pradeep Dasigi,Jesse Dodge, Dirk Groeneveld, Yuling Gu, Ananya Harsh Jha, Tushar Khot and Nishant Subramani
    58Extended AbstractGUMSum: Multi-Genre Data and Evaluation for English Abstractive SummarizationYang Janet Liu and Amir Zeldes
    60Extended AbstractNewsMet: A ‘Do It All' dataset of contemporary Metaphors in News headlinesRohan Joseph, Timothy Liu, Aik Beng Ng, Simon See and Sunny Rai
    20Extended AbstractOn the State of German (Abstractive) Text SummarizationDennis Aumiller, Jing Fan and Michael Gertz
    31Extended AbstractMeasuring misogyny in natural language generation: preliminary results from a case study on two Reddit communitiesAaron Snoswell, Lucinda Nelson, Hao Xue, Flora Salim, Nicolas Suzor and Jean Burgess
    35Extended AbstractOn the Learnability of Watermarks for Language ModelsChenchen Gu, Xiang Lisa Li, Percy Liang and Tatsunori Hashimoto
    47Extended AbstractDoes Writing with Language Models Reduce Content Diversity?Vishakh Padmakumar and He He
    39Extended AbstractGenerative language models exhibit social identity biasesTiancheng Hu, Yara Kyrychenko, Jon Roozenbeek and Nigel Collier
    70Industry TrackA Simple yet Efficient Ensemble Approach for AI-generated Text DetectionHarika Abburi, Kalyani Roy, Michael Suesserman, Nirmala Pudota, Balaji Veeramani, Edward Bowen and Sanmitra Bhattacharya
    17Industry TrackLeveraging Large Language Models for Enhanced Product Descriptions in eCommerceJianghong Zhou, Bo Liu, Jhalak Nilesh Acharya, Yao Hong, Kuang-chih Lee and Musen Wen
    55Industry TrackSeparating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in textIsaac Caswell, Lisa Wang and Isabel Papadimitriou
    +

    Organization

    Contact: gem-benchmark-chairs@googlegroups.com

    General Chairs

    @@ -59,4 +530,4 @@

    Organization

    Industry Track Chairs

    Enrico Santus (Bloomberg)

    Hooman Sedghamiz (Bayer)

    -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/workshop/2021.html b/workshop/2021.html index d4b292aa..9b5ee695 100644 --- a/workshop/2021.html +++ b/workshop/2021.html @@ -1,4 +1,4 @@ -GEM Workshop 2021
    GEM Workshop at ACL 2021

    The workshop will be held as part of ACL-IJCNLP 2021, August 1-6, 2021. It will take place on August 6. It is endorsed by the ACL Special Interest Group on Natural Language Generation (SIGGEN).

    +GEM Workshop 2021
    GEM Workshop at ACL 2021

    The workshop will be held as part of ACL-IJCNLP 2021, August 1-6, 2021. It will take place on August 6. It is endorsed by the ACL Special Interest Group on Natural Language Generation (SIGGEN).

    Note: Our system output submission form is perpetually open, please continue contributing to our benchmark. If you want to help improve GEM in the future, join our team.

    Workshop Overview

    Natural language generation is one of the most active research fields in NLP, with generation, summarization, and dialog among the most submitted-to tracks. As such, the number of available datasets, metrics, models, and evaluation strategies are increasing rapidly. This is leading to the situation where new models are often evaluated on different anglo-centric tasks with incompatible evaluation setups. With GEM, we are aiming to solve this problem by standardizing and improving the corpora on which to evaluate NLG models, and by supporting the development of better evaluation approaches. Submitted papers analyze the state of NLG evaluation and propose better alternatives. Moreover, we are organizing the living GEM benchmark which incorporates new advances in data and human and automatic evaluation to make it easier to evaluate models on challenging tasks with the correct tools. In our shared task, models were applied to up to 11 tasks in 18 languages, 80 challenge sets, and their outputs characterized using a combination of human evaluation and over 50 automatic metrics. @@ -213,4 +213,4 @@

    Organization

  • Wei Xu (Georgia Tech)

    • The shared task and the GEM environment is organized by a larger team which is listed on this page.

    -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/workshop/2022-call.html b/workshop/2022-call.html index efc16fb3..507c8846 100644 --- a/workshop/2022-call.html +++ b/workshop/2022-call.html @@ -1,4 +1,4 @@ -GEM Workshop 2022
    GEM Workshop at EMNLP 2022

    The GEM 💎 Workshop at EMNLP 2022

    +GEM Workshop 2022
    GEM Workshop at EMNLP 2022

    The GEM 💎 Workshop at EMNLP 2022

    The Second Version of Generation, Evaluation & Metrics (GEM) Workshop 2022 workshop will be held as part of EMNLP, December 7-11, 2022. It is endorsed by the ACL Special Interest Group on Natural Language Generation (SIGGEN).

    Overview

    Natural language generation (NLG) is one of the most active research fields in NLP. Yet, much of the work is focused on English, and too little attention is given to evaluation processes. As many of the recent developments in few-shot and in-context learning have led to the treatment of many tasks as text generation problems, the need for better NLG evaluation processes is becoming more urgent. To that end, the GEM workshop aims to encourage the development of (semi-) automatic model audits and improved human evaluation strategies, and to popularize model evaluations in languages beyond English.

    @@ -53,4 +53,4 @@

    Organization

  • Esin Durmus (Stanford University)
  • Samira Shaikh (UNC Charlotte)
    • -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/workshop/2022.html b/workshop/2022.html index efcb2e35..ad467e0d 100644 --- a/workshop/2022.html +++ b/workshop/2022.html @@ -1,4 +1,4 @@ -GEM Workshop 2022
    GEM 💎 Workshop at EMNLP 2022

    The Second Version of Generation, Evaluation & Metrics (GEM) Workshop 2022 workshop will be held as part of EMNLP, December 7, 2022. It is endorsed by the ACL Special Interest Group on Natural Language Generation (SIGGEN).

    +GEM Workshop 2022
    GEM 💎 Workshop at EMNLP 2022

    The Second Version of Generation, Evaluation & Metrics (GEM) Workshop 2022 workshop will be held as part of EMNLP, December 7, 2022. It is endorsed by the ACL Special Interest Group on Natural Language Generation (SIGGEN).

    The workshop will be held in hybrid mode with sessions in-person and via the conference portal.

    Schedule

    All times in Gulf Standard Time, please use a converter like this one to convert to your local time. @@ -523,4 +523,4 @@

    Organization

  • Esin Durmus (Stanford University)
  • Samira Shaikh (UNC Charlotte)
    • -
    \ No newline at end of file +
    \ No newline at end of file diff --git a/workshop/2023-call.html b/workshop/2023-call.html new file mode 100644 index 00000000..9f94b516 --- /dev/null +++ b/workshop/2023-call.html @@ -0,0 +1,62 @@ +GEM Workshop 2022
    GEM Workshop at EMNLP 2023

    The Third Version of the Generation, Evaluation & Metrics (GEM) Workshop will be held as part of EMNLP, December 6-10, 2023.

    +

    Overview

    +

    Many new NLP applications are cast through the lens of natural language generation. With the advent of these new approaches, many opportunities arise: generation in previously less studied languages, new evaluation paradigms, methods for corpus creation, more efficient architectures, strategies for safe deployments, among many others. At the same time, we can learn from the rich history of NLG research to further improve generation methods. +These developments require robust and sound NLG evaluation processes. To that end, the GEM workshop aims to encourage the development of model auditing and human evaluation strategies, and to popularize model evaluations in languages beyond English.

    +

    We welcome submissions related, but not limited to, the following topics:

    +
      +
    • 💎 Automatic evaluation of generation systems (example, example, example)
      • +
    • 💎 Creating NLG corpora and challenge sets (example, example, example)
      • +
    • 💎 Critiques of benchmarking efforts and responsibly measuring progress in NLG (example, example)
      • +
    • 💎 Effective and/or efficient NLG methods that can be applied to a wide range of languages and/or scenarios (example, example, example)
      • +
    • 💎 Application and evaluation of generation models interacting with external data and tools (example, example, example)
      • +
    • 💎 Sociotechnical perspectives of employing large language models (example)
      • +
    • 💎 Standardizing human evaluation and making it more robust (example, example, example)
      • +
    +

    We further invite submissions that conduct in-depth analyses of outputs of existing systems, for example through error analyses, by applying new metrics, or by testing the system on new test sets. While we encourage the use of the infrastructure the organizing team has developed as part of the GEM benchmark, its use is not required.

    +

    If you are interested, you can check out last year's workshop websites from ACL 2021 and EMNLP 2022.

    +

    Industrial Track - Unleashing the Power of NLP: Bridging the Gap between Academia and Industry

    +

    GEM 2023 is proud to announce the launch of its Industrial Track, which aims to provide actionable insights to industry professionals and to foster collaborations between academia and industry. This track will address the unique challenges faced by non-academic colleagues, highlighting the differences in evaluation practices between academic and industrial research, and explore the challenges in evaluating generative models with real-world data.

    +

    The Industrial Track invites submissions covering the following topics, including (but not limited to):

    +
      +
    • 💎 Breaking Barriers: Bridging the Gap between Academic and Industrial Research (example)
      • +
    • 💎 From Data Diversity to Model Robustness: Challenges in Evaluating Generative Models with Real-World Data (example)
      • +
    • 💎 Beyond Metrics: Evaluating Generative Models for Real-World Business Impact (example, example, example)
      • +
    +

    How to submit?

    +

    Submissions can take either of the following forms:

    +
      +
    • 💎 Archival Papers Papers describing original and unpublished work can be submitted in a between 4 and 8 page format.
      • +
    • 💎 Non-Archival Abstracts To discuss work already presented or under review at a peer-reviewed venue, we allow the submission of 2-page abstracts.
      • +
    +

    All submissions are allowed unlimited space for references and appendices and should conform to EMNLP 2023 style guidelines. Archival paper submissions must be anonymized while abstract submissions may include author information.

    +

    You can submit directly through SoftConf. Please select the track you are submitting to during the submission.

    +

    We additionally welcome presentations by authors of papers in the Findings of the EMNLP. The selection process is managed centrally by the workshop chairs for the conference and we thus cannot respond to all individual inquiries. However, we will try our best to accomodate your requests.

    +

    Shared Task

    +

    We are organizing a shared task focused on multilingual summarization, including human and automatic evaluation. The Shared Task will be run "Backwards": the workshop will serve as a platform to pre-register your hypotheses. More info on how to participate to come!

    +

    Important Dates

    +

    Note: For any questions, please email gem-benchmark-chairs@googlegroups.com.

    +

    Paper Submission Dates

    +
      +
    • 📅 8 September 2023: Workshop paper submission deadline
      • +
    • 📅 20 October 2023: Workshop paper notification deadline
      • +
    • 📅 3 November 2023: Workshop paper camera ready deadline
      • +
    +

    Note The website showed wrong dates for notication and CR deadlines. Apologies for any inconvenience.

    +

    Workshop Dates

    +
      +
    • 📅 6 December 2022: Workshop
      • +
    +

    Organization

    +

    Contact: +gem-benchmark-chairs@googlegroups.com

    +

    General Chairs

    +

    Khyathi Raghavi Chandu (AI2)

    +

    Elizabeth Clark (Google Deepmind)

    +

    Kaustubh Dhole (Emory University)

    +

    Sebastian Gehrmann (Bloomberg)

    +

    João Sedoc (NYU)

    +

    Alex Wang (Cohere)

    +

    Industry Track Chairs

    +

    Enrico Santus (Bloomberg)

    +

    Hooman Sedghamiz (Bayer)

    +
    \ No newline at end of file