Semantic Graphs for Generating Deep Questions

This repository contains code and models for the paper: Semantic Graphs for Generating Deep Questions (ACL 2020). Below is the framework of our proposed model (on the right) together with an input example (on the left).

Requirements

Environment

pytorch 1.4.0
nltk 3.4.4
numpy 1.18.1
tqdm 4.32.2

Data Preprocessing

We release all the datasets below which are processed based on HotpotQA.

get tokenized data files of documents, questions, answers, and the results of Dependency Parsing and Coreference Resolution on documents
- get results in folder text-data
prepare the json files ready as illustrated in build-semantic-graphs
- get results in folder json-data
run scripts/preprocess_data.sh to get the preprocessed data ready for training
- get results in folder preprocessed-data and folder Datasets
- utilize glove.840B.300d.txt from GloVe to initialize the word-embeddings

Models

We release both classifier and generator models in this work. The models are constructed based on a sequence-to-sequence architecture. Typically, we use GRU and GNN in the encoder and GRU in the decoder, you can choose other methods (e.g. Transformer) which have also been implemented in our repository.

classifier: accuracy - 84.06773%
generator: BLeU-4 - 15.28304

Training

run scripts/train_classifier.sh to train on the Content Selection task
run scripts/train_generator.sh to train on the Question Generation task, the default one is to finetune based on the pretrained classifier

Translating / Testing

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

Citation

    @article{pan2019sgdqg,
      title={Semantic Graphs for Generating Deep Questions},
      author={Liangming Pan and Yuxi Xie and Yansong Feng and Tat-Seng Chua and Min-Yen Kan},
      journal={ACL 2020},
      year={2020}
    }

calculusoflambdas/SG-Deep-Question-Generation