DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank

This repository contains the implementation of the paper:

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank [Paper] [OpenReview] [ACL Anthology] [arXiv]
Findings of the Association for Computational Linguistics: EMNLP 2023
Henry Peng Zou, Yue Zhou, Weizhi Zhang, Cornelia Caragea

News

🌱 Welcome to check out our other work on semi-supervised learning: JointMatch!

Setup

Install Package

conda create -n decrisis python=3.8 -y
conda activate decrisis

# install pytorch
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -c pytorch

# install dependency
pip install -r requirements.txt

Data Preparation

All data is included in this repository. The file structure should look like:

DeCrisis/
    |-- data/
        |-- hurricane
        |-- threecrises
        |-- ag_news
            |-- train.csv
            |-- val.csv
            |-- test.csv
            ......
    |-- models
    |-- utils
    |-- main.py
    |-- panel_main.py 
    ......

Reproduce Paper Results

To reproduce our main paper results, simply run:

python panel_main.py
python panel_threecrises.py

To reproduce our out-of-domain results, simply run:

python panel_ODomain.py

Specify the log location and model saving location if you need, e.g., log_home = './experiment/hurricane' in panel_main.py and output_dir_path = './experiment/hurricane' in main.py.

Bugs or Questions

If you have any questions related to the code or the paper, feel free to email Henry Peng Zou (pzou3@uic.edu). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

Citation

If you find this repository helpful, please consider citing our paper 💕:

@inproceedings{zou2023decrisismb,
    title={DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank},
    author={Zou, Henry and Zhou, Yue and Zhang, Weizhi and Caragea, Cornelia},
    booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
    pages={6104--6115},
    year={2023}
}

@inproceedings{zou2023jointmatch,
    title={JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification},
    author={Zou, Henry and Caragea, Cornelia},
    booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
    pages={7290--7301},
    year={2023}
}

Acknowledgement

This repo borrows some data and codes from DebiasPL and USB. We appreciate their great works.
Besides, welcome to check out our other work in semi-supervised learning: JointMatch!