/neural_question_generation

Implementation of <Learning to Ask: Neural Question Generation for Reading Comprehension> by Xinya Du et al.

Primary LanguagePython

neural_question_generation

Implemenration of <Learning to Ask: Neural Question Generation for Reading Comprehension> by Xinya Du et al.

The source code still needs to be modified

  1. Model

  • Embedding

    • Pretrained GloVe embeddings
    • Randomly initialized embeddings

  • RNN-based seq2seq

    • GRU/LSTM

  • To be updated

    • Beam decoder
    • Post-processing code for unknown words
  1. Dataset

processed data provided by Xinya Du et al.

Requirements

  • python 2.7
  • numpy
  • Tensorflow 1.4

Usage

  1. Data preprocessing
mkdir data/processed
python process_data.py
  1. Download & process GloVe
wget http://nlp.stanford.edu/data/glove.840B.300d.zip -P data/
unzip data/glove.840B.300d.zip -d data/
python process_embedding.py # This will take a couple of minutes
  1. Train model
# data_name : dataset name in run.sh
# model_name : the name of weight set, you can train multiple models with the same parameters by taking different model name 
bash run.sh [data_name] train [model_name]
# example : bash run.sh squad train model_0
  1. Test model
mkdir result # only for the first time
bash run.sh [data_name] pred [model_name]
# example : bash run.sh squad pred model_0
# the result will be saved in the result folder