Neural Question Generation

This is not official implementation for the paper Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks. I implemented in Pytorch to reproduce similar result as the paper. You can find the checkpoint of pretrained model here.

Dependencies

This code is written in Python. Dependencies include

• python >= 3.6
• pytorch >= 1.4
• nltk
• tqdm
• pytorch_scatter

Download data and Preprocess

mkdir squad
wget http://nlp.stanford.edu/data/glove.840B.300d.zip -O ./data/glove.840B.300d.zip 
unzip ./data/glove.840B.300d.zip 
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json -O ./squad/train-v1.1.json
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json -O ./squad/dev-v1.1.json
cd data
python process_data.py

Configuration

You might need to change configuration in config.py.
If you want to train, change train = True and set the gpu device in config.py

Evaluation from this repository

cd qgevalcap
python2 eval.py --out_file prediction_file --src_file src_file --tgt_file target_file

Results

BLEU_1	BLEU_2	BLEU_3	BLEU_4
45.22	29.94	22.01	16.76