The source code used for paper PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training, published in EMNLP 2023.

PIEClass is a weakly-supervised text classifier with prompting and iterative ensemble training. Unlike previous methods, PIEClass utilizes PLM prompting to go beyond keyword-level pseudo labeling and iterative fine-tuning prompt-based and head-token-based classifiers to expand pseudo labels while largely ensure their quality. For more details, please check our paper: https://arxiv.org/abs/2305.13723.

GPU is required for running PIEClass.

We develop the code with Python 3.8. Before running, you need to first install the required packages (within a virtual environment is recommended):

pip3 install -r requirements.txt

We use seven benchmmark datasets: AGNews, 20News, NYT-Topics, and Yelp can be found at here; IMDB and Amazon can be found at here; we include the NYT-Fine corpus in the ./datasets/NYT-Fine/ folder.

We provide the scripts to reproduce our results on the benchmark datasets in ./scripts/. You can run them in the root directory by:

./scripts/AGNews.sh

If you find our work useful for your research, please cite the following paper:

@inproceedings{zhang2023pieclass,
    title={{PIEC}lass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training}, 
    author={Yunyi Zhang and Minhao Jiang and Yu Meng and Yu Zhang and Jiawei Han},
    year={2023},
    booktitle={EMNLP}
}