Text-AutoAugment (TAA)
This repository contains the code for our paper Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (EMNLP 2021 main conference).
Overview
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We present a learnable and compositional framework for data augmentation. Our proposed algorithm automatically searches for the optimal compositional policy, which improves the diversity and quality of augmented samples.
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In low-resource and class-imbalanced regimes of six benchmark datasets, TAA significantly improves the generalization ability of deep neural networks like BERT and effectively boosts text classification performance.
Getting Started
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Prepare environment
conda create -n taa python=3.6 conda activate taa conda install pytorch torchvision cudatoolkit=10.0 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch pip install -r requirements.txt python -c "import nltk; nltk.download('wordnet'); nltk.download('averaged_perceptron_tagger')" -
Modify
datarootparameter inconfs/*yamlandabspathparameter inscript/*.sh:- e.g., change
dataroot: /home/renshuhuai/TextAutoAugment/data/aclImdbin confs/bert_imdb.yaml todataroot: path-to-your-TextAutoAugment/data/aclImdb - change
--abspath '/home/renshuhuai/TextAutoAugment'in script/imdb_lowresource.sh to--abspath 'path-to-your-TextAutoAugment'
- e.g., change
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Search for the best augmentation policy, e.g., low-resource regime for IMDB:
sh script/imdb_lowresource.sh
scripts for policy search in the low-resource and class-imbalanced regime for all datasets are provided in the script/ fold.
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Train a model with pre-searched policy in archive.py, e.g., train model in low-resource regime for IMDB:
python train.py -c confs/bert_imdb.yaml
train model on full dataset of IMDB:
python train.py -c confs/bert_imdb.yaml --train-npc -1 --valid-npc -1 --test-npc -1
Contact
If you have any questions related to the code or the paper, feel free to email Shuhuai (renshuhuai007 [AT] gmail [DOT] com).
Acknowledgments
Code refers to: fast-autoaugment.
Citation
If you find this code useful for your research, please consider citing:
@inproceedings{ren2021taa,
title={Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification},
author={Shuhuai Ren, Jinchao Zhang, Lei Li, Xu Sun, Jie Zhou},
booktitle={EMNLP},
year={2021}
}
License
MIT
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