Boosting Data Augmentation for Text Classification

Codes for ACL2023 Findings paper: Boosting Text Augmentation via Hybrid Instance Filtering Framework

Usage in PyABSA

you can find examples for augmenting text classification and aspect-term sentiment classification at https://github.com/yangheng95/PyABSA/tree/v2/examples-v2/text_augmentation

Notice

This tool depends on the PyABSA, and is integrated with the ABSADatasets.

To augment your own dataset, you need to prepare your dataset according to ABSADatasets. Refer to the instruction to process or annotate your dataset.

Install BoostAug

Install from source

git clone https://github.com/yangheng95/BoostTextAugmentation

cd BoostTextAugmentation

pip install .

MUST READ

• If the augmentation traning is terminated by accidently or you want to rerun augmentation, set rewrite_cache=True in augmentation.
• If you have many datasets, run augmentation for differnet datasets IN SEPARATE FOLDER, otherwise IO OPERATION may CORRUPT other datasets

Notice

This is the draft code, so do not perform cross-boosting on different dataset in the same folder, which will raise some Exception

Citation

@inproceedings{yang-li-2023-boosting,
    title = "Boosting Text Augmentation via Hybrid Instance Filtering Framework",
    author = "Yang, Heng  and
      Li, Ke",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.105",
    pages = "1652--1669",
}