ERIQG

Code implementation for the text infilling model in our work Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates.

Requirements

• python2.7.
• PyTorch 0.4.0.
• CUDA 9.0
• The code has been tested on GTX 1080 Ti running on Ubuntu 16.04.4 LTS.

Training and Testing

• Pre-trained model and word embeddings can be downloaded from here.

• Copy eriqg_best.pth.tar to code folder.

• Copy usedwordemb.npy to glove folder.

• To train the model by running: python train.py --batch_size=16 --teacher_forcing_fraction 1.0 --prefix 'eriqg' on terminal.

• To test on pre-trained model by running: python evaluation.py --batch_size=16 --teacher_forcing_fraction 0.0 --resume eriqg_best.pth.tar on terminal.

Citation

If you use ERIQG, please cite the following work:

@inproceedings{yu2021expanding,
  title={Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates},
  author={Yu, Xiaojing and Jiang, Anxiao},
  booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  pages={3202--3212},
  year={2021}
}