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RAST

This repository contains code for the paper Diversify Question Generation with Retrieval-Augmented Style Transfer

  • we provide our processed_data in data_link.
  • we also provide our model checkpoint in checkpoint_link.
  • if you use our repository, please cite paper. If you find this code useful in your research, please consider citing:
@misc{gou2023diversify,
      title={Diversify Question Generation with Retrieval-Augmented Style Transfer}, 
      author={Qi Gou and Zehua Xia and Bowen Yu and Haiyang Yu and Fei Huang and Yongbin Li and Nguyen Cam-Tu},
      year={2023},
      eprint={2310.14503},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

To reproduce

1. download data

2. process data

  • process original data
python data/process_data.py  
refer to  data/readme.md  
  • convert and store corpus data into faiss vector
python rast/rag/prepare_dataset.py
refer to rast/rag/prepare_dataset.py

3. train generator with skeleton

refer to rast/qg/readme.md 

4. train vanilla generator

refer to rast/qg/readme.md 

5. train QA model

refer to rast/reward_mdoel/T5_QA/readme.md

6. train rag

refer to rast/rag/readme_v100.md

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