readme.md

Repository for NLU project course academic year 2021/2022

In this repository there will be contained everything used for the project:

• source code
• report
• datasets

Task

The task to perform both Slot filling and Intent classification tasks.

In the literature there are proofs that models trained jointly on both tasks perform better than individual task specific models.

Slot filling

The sequence labelling task is defined as:

• Given a sequence of tokens $w = {w_1, w_2, ..., w_n}$,
• defining a sequence of labels as $l = {l_1, l_2, ..., l_n}$
• compute the sequence $\hat{l}$ such as $\hat{l} = \underset{l}{\text{argmax}} P(l|w)$

An example from ATIS dataset is the following:

Slot Filling
Input sequence:	i	'd	like	to	see	all	the	economy	fares	from	baltimore	to	philadelphia
Output sequence:	O	O	O	O	O	O	O	B-economy	O	O	B-fromloc.city_name	O	B-toloc.city_name

Intent classification

The intent classification problem is defined as follows:

• Given a sequence of tokens $w = {w_1, w_2, ..., w_n}$,
• And a set of labels $L$ where $l \in L$
• estimate the label $\hat{l}$ such as $\hat{l} = \underset{l}{\text{argmax}} P(l|w)$

An example from ATIS dataset is the following:

Intent Classification
Input sequence:	i	'd	like	to	see	all	the	economy	fares	from	baltimore	to	philadelphia
Output label:	airfare

Dataset

The datasets that are used in the evaluation are ATIS (Airline Travel Information Systems) and SNIPS

ATIS

Airline Travel Information Systems (ATIS) dataset is made of entries as such

[
    {
        "utterance": "i want to fly from boston at 838 am and arrive in denver at 1110 in the morning",
        "slots": "O O O O O B-fromloc.city_name O B-depart_time.time I-depart_time.time O O O B-toloc.city_name O B-arrive_time.time O O B-arrive_time.period_of_day",
        "intent": "flight"
    },
    ...
]

SNIPS

SNIPS dataset is made of entries as such

[
    {
        "utterance": "listen to westbam alumb allergic on google music",
        "slots": "O O B-artist O B-album O B-service I-service",
        "intent": "PlayMusic"
    },
    ...
 ]

Models

Baseline LSTM

The first model to be evaluated is the model from LAB 10 of the course. It consists in a simple LSTM architecture, with two linear fully connected layers, one for each task.

Results:

ATIS		Balanced ATIS		SNIPS
Slot F1	Intent Accuracy	Slot F1	Intent Accuracy	Slot F1	Intent Accuracy
92.06	93.34	94.43	96.71	80.27	96.05

Slot embeddings ATIS

Intent embeddings ATIS

Slot embeddings SNIPS

Intent embeddings SNIPS

BiLSTM

A slighlty improved LSTM over the baseline model:

• bidirectional LSTM
• increased size of embedding
• increased size of hidden
• increased number of layers for the LSTM
• layer normalization
• dropout
• better early stopping criteria, on both losses instead of slot filling f1 score
• increase number of epochs ( which is not a problem due to early stopping )

Plus:

• Randomly Weighted Loss
• Focal Loss

However it uses a pseudo randomly weighting, with the higher weight applied to the higher loss. This should help improve the results, as higher losses will be weighted more and therefore the model should learn better.

These improvements do not change drastically the architecture. Rather, they are small tweaks that allow to get a few points more. More precise tuning of the hyperparameters could further increase the score.

Results:

ATIS		Balanced ATIS		SNIPS
Slot F1	Intent Accuracy	Slot F1	Intent Accuracy	Slot F1	Intent Accuracy
94.59	94.89	97.52	97.24	87.06	96.14

Slot embeddings ATIS

Intent embeddings ATIS

Slot embeddings SNIPS

Intent embeddings SNIPS

BERT

Bidirectional Encoder Representations from Transformers (BERT) allow to encode sentences better than Recurrent models and at the same time being faster. I use a pre-trained BERT to extract embeddings and train a simple network to perfom the task

Results:

ATIS		Balanced ATIS		SNIPS
Slot F1	Intent Accuracy	Slot F1	Intent Accuracy	Slot F1	Intent Accuracy
95.50	97.31	98.36	98.43	94.23	97.71

Slot embeddings ATIS

Intent embeddings ATIS

Slot embeddings SNIPS

Intent embeddings SNIPS