Exploring Question-Specific Rewards for Generating Deep Questions

This repository contains codes and models for the paper: Exploring Question-Specific Rewards for Generating Deep Questions (COLING 2020 oral). Below is the framework of our proposed model.

Requirements

Environment

pytorch 1.4.0
nltk 3.4.4
numpy 1.18.1
tqdm 4.32.2

Data Preprocessing & Models

download here

Training

run scripts/train_example.sh to train the ensemble model which utilizes all three rewards.

Translating

run scripts/translate_example.sh to get the prediction on the validation dataset.

Evaluating

We take use of the Evaluation codes for MS COCO caption generation for evaluation on automatic metrics.

To install pycocoevalcap and the pycocotools dependency, run:

pip install git+https://github.com/salaniz/pycocoevalcap

To evaluate the results in the translated file, e.g. prediction.txt, run:

python evaluate_metrics.py prediction.txt

Citation

    @inproceedings{xie-etal-2020-RLQG,
      title = {Exploring Question-Specific Rewards for Generating Deep Questions},
      author = {Xie, Yuxi and Pan, Liangming and Wang, Dongzhe and Kan, Min-Yen and Feng, Yansong},
      booktitle = {The 28th International Conference on Computational Linguistics (COLING 2020)},
      year = {2020}
    }

teacherpeterpan/RL-for-Question-Generation