Dual Co-Matching Network for multiple choice questions

This repo contains an re-implementation of Dual Co-Matching Network for multiple choice questions introduced in this paper. The model uses BERT-Small with 4 layers of encoders and a hidden size of 256, and achieves accuracy around 0.6138. The model definition is stored in model.py, and main.py is the script for running the training task. The current model is trained with a subset of the high school questions in the RACE dataset.

References

Original Paper:

@inproceedings{dcmn,
    title={DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension},
    author={Shuailiang Zhang and Hai Zhao and Yuwei Wu and Zhuosheng Zhang and Xi Zhou and Xiang Zhou},
    year={2020},
    booktitle = "{The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)}",
}

Dataset:

@article{lai2017large,
    title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},
    author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},
    journal={arXiv preprint arXiv:1704.04683},  
    year={2017}
}