hindi-nli-code

Implementation of the AACL-IJCNLP 2020 paper: Two-Step Classification using Recasted Data for Low Resource Settings.

Requirements

All the code in this repo is built with PyTorch.

python3.5+
pytorch1.4.0
numpy
pdb

Data

After downloading the data, use the arguments train_data , test_data and val_data in the scripts in order to point to the directory containing the respective .tsv files.

Training

To independently train the Textual Entailment model (TE) without the joint objective, use

python nli_train.py

To train the Textual Entailment model along with Two-Step Classification (i.e. with the joint objective - TE + JO), use

python nli_train_joint.py

In order to train using the consistency regularization technique (+CR), use the argument is_cr=True, else turn is_cr=False.

To train the Direct Classification model, use

python clf_train.py

Testing

To evaluate the accuracy of the trained models for both Textual Entailment and Classification, run the script python evaluate.py in their respective folders.

To evaluate the inconsistency results, run the script python inconsistency.py in the Textual Entailment folder.

To evaluate the comparison results between Direct Classification and Two-Step Classification approaches, run the script python comparison.py in the Textual Entailment folder.

For results in the semi-supervised setting (appendix), use the desired percentage from the training data without modifying test and dev sets.

Following is a guide to the command line arguments that can help training with the desired setting:

• train_data - Dataset directory followed by the file containing training data
• test_data - Dataset directory followed by the file containing test data
• val_data - Dataset directory followed by the file containing validation data
• n_classes_clf - Number of classes in the original classification task of the dataset being used
• max_train_sents - Maximum number of training examples
• max_test_sents - Maximum number of testing examples
• max_val_sents - Maximum number of validation examples
• n_epochs - Number of epochs to run the training for
• n_classes - Number of classes for the textual entailment task, which is 2 irrespective of the dataset (entailed and not-entailed)
• n-sentiment - Number of classes for the classification task
• batch_size - Number of data samples in the batch for each iteration
• dpout_model - Dropout rate for the encoder network
• dpout_fc - Dropout rate for the classifier network
• optimizer - To choose the type of the optimizer for training (SGD or Adam)
• lr_shrink - Shrink factor for SGD
• decay - Decay factor for learning rate
• minlr - Minimum learning rate
• is_cr - True for training with consistency regularization, otherwise False
• embedding_size - Embedding size of the sentence embedding model used
• max_norm - Maximum norm for the gradients

Bibliograhy

@inproceedings{uppal-etal-2020-two,
    title = "Two-Step Classification using Recasted Data for Low Resource Settings",
    author = "Uppal, Shagun  and
      Gupta, Vivek  and
      Swaminathan, Avinash  and
      Zhang, Haimin  and
      Mahata, Debanjan  and
      Gosangi, Rakesh  and
      Shah, Rajiv Ratn  and
      Stent, Amanda",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
    pages = "706--719",
    abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",