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

  • Post-processing code for unknown words

2. 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

2. 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

3. Train model

# data_name : dataset name which is defined in run.sh
# hyperparameters : hyperparameters setting which is defined in params.py
# epochs: training epochs

bash run.sh train [data_name] [hyperparameters] [epochs]
# example : bash run.sh train squad basic_params 10

4. Test model

mkdir result # only for the first time, predicted result will be saved here
bash run.sh pred [data_name] [hyperparameters] 0 
# example : bash run.sh pred squad basic_params 0