Source code for ACL 2022 paper: Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks

Our work mainly based on Data Augmentation using Pre-trained Transformer Models

Code contains implementation of the following data augmentation methods

• TextSmoothing
• EDA + TextSmoothing
• Backtranslation + TextSmoothing
• CBERT + TextSmoothing
• BERT Prepend + TextSmoothing
• GPT-2 Prepend + TextSmoothing
• BART Prepend + TextSmoothing

DataSets

In paper, we use three datasets from following resources

• STSA-2 : https://github.com/1024er/cbert_aug/tree/crayon/datasets/stsa.binary
• TREC : https://github.com/1024er/cbert_aug/tree/crayon/datasets/TREC
• SNIPS : https://github.com/MiuLab/SlotGated-SLU/tree/master/data/snips

Low-data regime experiment setup

Run src/utils/download_and_prepare_datasets.sh file to prepare all datsets. download_and_prepare_datasets.sh performs following steps

1. Download data from github
2. Replace numeric labels with text for STSA-2 and TREC dataset
3. For a given dataset, creates 15 random splits of train and dev data.

Dependencies

To run this code, you need following dependencies

• Pytorch 1.5
• fairseq 0.9
• transformers 2.9

How to run

To run data augmentation experiment for a given dataset, run bash script in scripts folder. For example, to run data augmentation on snips dataset,

• run scripts/bart_snips_lower.sh for BART experiment
• run scripts/bert_snips_lower.sh for rest of the data augmentation methods