Answer Generation Implementation - https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf Question Generation Implementation - http://www.aclweb.org/anthology/N18-2090

The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). Pretrained GloVe embeddings are used for both words (https://nlp.stanford.edu/projects/glove/) and characters (https://github.com/minimaxir/char-embeddings/blob/master/glove.840B.300d-char.txt).

• Python2.7
• NumPy
• tqdm
• spacy
• tensorflow-gpu==1.12
• graphviz
• pythonrogue

Once you clone this repo, run the following lines from bash just once to process the dataset (SQuAD).

$ pipenv install
$ bash setup.sh
$ pipenv shell
$ python process.py --reduce_glove True --process True

[{"text1":"IBM is headquartered in Armonk , NY .", 
 {"text2":"Where is IBM located ?", 
 {"text3":"Armonk , NY"
}]

where "text1" and "annotation1" correspond to the text and rich annotations for the passage. Similarly, "text2" and "text3" correspond to the question and answer parts, respectively.

$ python model.py

python src/NP2P_trainer.py --config_path config.json

where config.json is a JSON file containing all hyperparameters. We attach a sample config file along with our repository.

After training, change mode="train" to debug or test from params.py file and run the model.

python NP2P_beam_decoder.py --model_prefix xxx --in_path yyy --out_path zzz --mode beam

Run the script - python demo.py