QA-GNN: Question Answering using Language Models and Knowledge Graphs

This repo provides the source code & data of our paper: QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering (NAACL 2021).

@InProceedings{yasunaga2021qagnn,
  author =  {Michihiro Yasunaga and Hongyu Ren and Antoine Bosselut and Percy Liang and Jure Leskovec},
  title =   {QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering},
  year =    {2021},  
  booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)},  
}

Webpage: https://snap.stanford.edu/qagnn

Usage

0. Dependencies

• Python == 3.7
• PyTorch == 1.4.0
• transformers == 2.0.0
• torch-geometric ==1.6.0

Run the following commands to create a conda environment (assuming CUDA10.1):

conda create -n qagnn python=3.7
source activate qagnn
pip install numpy==1.18.3 tqdm
pip install torch==1.4.0 torchvision==0.5.0
pip install transformers==2.0.0 nltk spacy==2.1.6
python -m spacy download en

#for torch-geometric
pip install torch-scatter==2.0.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-cluster==1.5.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-sparse==0.6.1 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-spline-conv==1.2.0 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-geometric==1.6.0 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html

1. Download Data

Download all the raw data -- ConceptNet, CommonsenseQA, OpenBookQA -- by

./download_raw_data.sh

You can preprocess the raw data by running

python preprocess.py -p <num_processes>

The script will:

• Setup ConceptNet (e.g., extract English relations from ConceptNet, merge the original 42 relation types into 17 types)
• Convert the QA datasets into .jsonl files (e.g., stored in data/csqa/statement/)
• Identify all mentioned concepts in the questions and answers
• Extract subgraphs for each q-a pair

TL;DR. The preprocessing may take long; for your convenience, you can download all the processed data by

./download_preprocessed_data.sh

The resulting file structure will look like:

.
├── README.md
└── data/
    ├── cpnet/                 (prerocessed ConceptNet)
    └── csqa/
        ├── train_rand_split.jsonl
        ├── dev_rand_split.jsonl
        ├── test_rand_split_no_answers.jsonl
        ├── statement/             (converted statements)
        ├── grounded/              (grounded entities)
        ├── graphs/                (extracted subgraphs)
        ├── ...

2. Training

For CommonsenseQA, run

./run_qagnn__csqa.sh

For OpenBookQA, run

./run_qagnn__obqa.sh

As configured in these scripts, the model needs two types of input files

• --{train,dev,test}_statements: preprocessed question statements in jsonl format. This is mainly loaded by load_input_tensors function in utils/data_utils.py.
• --{train,dev,test}_adj: information of the KG subgraph extracted for each question. This is mainly loaded by load_sparse_adj_data_with_contextnode function in utils/data_utils.py.

Use Your Own Dataset

• Convert your dataset to {train,dev,test}.statement.jsonl in .jsonl format (see data/csqa/statement/train.statement.jsonl)
• Create a directory in data/{yourdataset}/ to store the .jsonl files
• Modify preprocess.py and perform subgraph extraction for your data
• Modify utils/parser_utils.py to support your own dataset

Acknowledgment

This repo is built upon the following work:

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering. Yanlin Feng*, Xinyue Chen*, Bill Yuchen Lin, Peifeng Wang, Jun Yan and Xiang Ren. EMNLP 2020.
https://github.com/INK-USC/MHGRN

Many thanks to the authors and developers!