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Low-Resource Text Classification Framework

Introduced in Ein-dor et al. (2020), this is a framework for experimenting with text classification tasks. The focus is on low-resource scenarios, and examining how active learning (AL) can be used in combination with classification models.

The framework includes a selection of labeled datasets, machine learning models and active learning strategies (see Built-in Implementations below), and can be easily adapted for additional setups and scenarios.

Table of contents

Installation

Running active learning experiments

Adapting to additional scenarios:

Built-in Implementations

Reference

License

Installation

Currently, the framework requires Python 3.7

  1. Clone the repository locally:

    git clone https://github.com/IBM/low-resource-text-classification-framework

  2. Install the project dependencies: pip install -r lrtc_lib/requirements.txt

    Windows users also need to download the latest Microsoft Visual C++ Redistributable for Visual Studio in order to support tensorflow

  3. Run the shell script lrtc_lib/download_and_prepare_datasets.sh. This script downloads the datasets with built-in support.

Running active learning experiments

The ExperimentRunner class enables running experiments in the vein of Ein-dor et al. (2020), i.e. an experimental flow where an initial seed of labeled instances is used to train a model, and then several iterations of active learning are performed. In each active learning iteration, the set of labeled instances is expanded with the batch examples selected by the active learning module, and a new model is trained on this larger set.

Implementations of ExperimentRunner vary in terms of how the initial seed of labeled instances is selected. The three scenarios described in the paper are implemented by:

  1. ExperimentRunnerBalanced
  2. ExperimentRunnerImbalanced
  3. ExperimentRunnerImbalancedPractical

The experiment flow can be performed on a custom combination of datasets, model types and active learning strategies.

To run an experiment from a terminal, go to the repository directory (usually <path_to_python_projects>/low-resource-text-classification-framework) and run python -m path.to.module, for example:

python -m lrtc_lib.experiment_runners.experiment_runner_imbalanced_practical

Alternatively, an IDE such as PyCharm can be used.

The main function of each ExperimentRunner specifies all the experimental parameters. For information on all the dataset and category names available for running experiments, run loaded_datasets_info.py using python -m lrtc_lib.data_access.loaded_datasets_info.

Adapting to additional scenarios

Implementing a new machine learning model

These are the steps for integrating a new classification model:

  1. Implement a new TrainAndInferAPI

    Machine learning models are integrated by adding a new implementation of the TrainAndInferAPI. The main functions are train and infer:

    Train a new model and return a unique model identifier that will be used for inference.

    def train(self, train_data: Sequence[Mapping], dev_data: Sequence[Mapping], test_data: Sequence[Mapping], 
train_params: dict) -> str
    • train_data - a list of dictionaries with at least the "text" and "label" fields. Additional fields can be passed e.g. [{'text': 'text1', 'label': 1, 'additional_field': 'value1'}, {'text': 'text2', 'label': 0, 'additional_field': 'value2'}]
    • dev_data: can be None if not used by the implemented model
    • test_data - can be None if not used by the implemented model
    • train_params - dictionary for additional train parameters (can be None)

    Infer a given sequence of elements and return the results.

    def infer(self, model_id, items_to_infer: Sequence[Mapping], infer_params: dict, use_cache=True) -> dict:
    • model_id
    • items_to_infer: a list of dictionaries with at least the "text" field. Additional fields can be passed, e.g. [{'text': 'text1', 'additional_field': 'value1'}, {'text': 'text2', 'additional_field': 'value2'}]
    • infer_params: dictionary for additional inference parameters (can be None)
    • use_cache: save the inference results to cache. Default is True

    Returns a dictionary with at least the "labels" key, where the value is a list of numeric labels for each element in items_to_infer. Additional keys (with list values of the same length) can be passed, e.g. {"labels": [1, 0], "gradients": [[0.24, -0.39, -0.66, 0.25], [0.14, 0.29, -0.26, 0.16]]}

  2. Specify a new ModelType in ModelTypes

  3. Return the newly implemented TrainAndInferAPI in TrainAndInferFactory

  4. The system assumes that active learning strategies that require special inference outputs (e.g. text embeddings) are not supported by your new model. If your model does support this, add it to the appropriate category in get_compatible_models in strategies.py

  5. Set your ModelType in one of the ExperimentRunners, and run

Implementing a new active learning strategy

These are the steps for integrating a new active learning approach:

  1. Implement a new ActiveLearner

    Active learning modules inherit from the ActiveLearner API. The main function to implement is get_recommended_items_for_labeling:

    def get_recommended_items_for_labeling(self, workspace_id: str, model_id: str, dataset_name: str,
                                        category_name: str, sample_size: int = 1) -> Sequence[TextElement]:

    This function returns a batch of sample_size elements suggested by the active learning module for a given dataset and category, based on the outputs of model model_id.

    Optionally, the ActiveLearner can also implement the function get_per_element_score, where the active learning module does not just return a batch of selected elements, but can also assign each text element with a score.

  2. Specify a new ActiveLearningStrategy in ActiveLearningStrategies

  3. Return your new ActiveLearner in ActiveLearningFactory

  4. If the active learner requires particular outputs from the machine learning model, update get_compatible_models accordingly. For instance, if the strategy relies on model embeddings, add it to the set of embedding-based strategies.

  5. Set your ActiveLearningStrategy in one of the ExperimentRunners, and run

Adding a new dataset

These are the steps for adding a new dataset:

  1. Split your dataset into 3 csv files: train.csv, dev.csv, and test.csv.

    1. Each line is a text element.
    2. Each file should have at least two columns: label and text, and may have additional columns.
    3. Files are placed under lrtc_lib/data/available_datasets/<new_dataset_name>

  2. Create a processor for the new dataset by extending CsvProcessor (which implements DataProcessorAPI) and place it under lrtc_lib/data_access/processors. CsvProcessor __init__ function looks like this:

    def __init__(self, dataset_name: str, dataset_part: DatasetPart, text_col: str = 'text',
              label_col: str = 'label', context_col: str = None,
              doc_id_col: str = None,
              encoding: str = 'utf-8'):
    • dataset_name: the name of the processed dataset
    • dataset_part: the part - train/dev/test - of the dataset
    • text_col: the name of the column which holds the text of the TextElement. Default is text.
    • label_col: the name of the column which holds the label. Default is label.
    • context_col: the name of the column which provides context for the text, None if no context is available. Default is None.
    • doc_id_col: column name by which text elements should be grouped into documents. If None, all text elements would be put in a single dummy doc. Default is None.
    • encoding: the encoding to use to read the csv raw file(s). Default is utf-8.

    For example, here is the processor for DBPedia (which uses the default values of CsvProcessor):

     class DbpediaProcessor(CsvProcessor):

 def __init__(self, dataset_part: DatasetPart):
     super().__init__(dataset_name='dbpedia', dataset_part=dataset_part)

    If more flexibility is needed, implement DataProcessorAPI directly.

  3. Add the new processor to data_processor_factory. Note, in this step you define the name of the new dataset.

  4. Run load_dataset with the new dataset name (as defined in data_processor_factory) to generate dump files under data/data_access_dumps (for the documents and text elements of the dataset) and data/oracle_access_dumps (for the gold labels of the text elements).

Built in Implementations

Datasets

* Loading the ISEAR dataset requires installing additional dependencies before running the installation script, and is only supported on Mac/Linux. Specifically, you will need to install mdbtools on your machine and then pip install pandas_access.

Classification models

  • ModelTypes.NB: a Naive Bayes implementation from scikit-learn
  • ModelTypes.HF_BERT: A tensorflow implementation of BERT (Devlin et al. 2018) that uses the huggingface Transformers library

Active learning strategies

  • RANDOM: AL baseline, randomly sample from unlabeled data.
  • RETROSPECTIVE: select the top scored instances by the model.
  • HARD_MINING: a.k.a uncertainty sampling / least confidence; Lewis and Gale 1994
  • GREEDY_CORE_SET: the greedy method from Sener and Savarese 2017
  • DAL: Discriminative representation sampling; Gissin and Shalev-Shwartz 2019
  • PERCEPTRON_ENSEMBLE: lightweight ensemble version of uncertainty sampling; uncertainty is determined using an ensemble of perceptrons, which were trained over output embeddings from the original model.
  • DROPOUT_PERCEPTRON: similar to the above, but instead of an ensemble of perceptrons, uses a single perceptron with Monte Carlo dropout (Gal and Ghahramani, 2016)

Reference

Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz and Noam Slonim (2020). Active Learning for BERT: An Empirical Study. EMNLP 2020

Please cite:

@inproceedings{ein-dor-etal-2020-active,
    title = "Active Learning for {BERT}: An Empirical Study",
    author = "Ein-Dor, Liat  and
      Halfon, Alon  and
      Gera, Ariel  and
      Shnarch, Eyal  and
      Dankin, Lena  and
      Choshen, Leshem  and
      Danilevsky, Marina  and
      Aharonov, Ranit  and
      Katz, Yoav  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.638",
    pages = "7949--7962",
}

License

This work is released under the Apache 2.0 license. The full text of the license can be found in LICENSE.