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[NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs 🤖
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# 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](https://arxiv.org/abs/2104.06378) (NAACL 2021).
```bib
@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](https://snap.stanford.edu/qagnn)
<p align="center">
<img src="./figs/task.png" width="400" title="Question answering task" alt="">
</p>
<p align="center">
<img src="./figs/overview.png" width="1000" title="Overview of QA-GNN" alt="">
</p>
## Usage
### 0. Dependencies
Run the following commands to create a conda environment (assuming CUDA10.1):
```bash
conda create -n qagnn python=3.7
source activate qagnn
pip install torch==1.8.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers==3.4.0
pip install nltk spacy==2.1.6
python -m spacy download en
# for torch-geometric
pip install torch-scatter==2.0.7 -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html
pip install torch-sparse==0.6.9 -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html
pip install torch-geometric==1.7.0 -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html
```
### 1. Download data
We use the question answering datasets (*CommonsenseQA*, *OpenBookQA*) and the ConceptNet knowledge graph.
Download all the raw data by
```
./download_raw_data.sh
```
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 (Skip above steps and just get preprocessed data)**. The preprocessing may take long. For your convenience, you can download all the processed data by
```
./download_preprocessed_data.sh
```
**🔴 NEWS (Add MedQA-USMLE)**. Besides the commonsense QA datasets (*CommonsenseQA*, *OpenBookQA*) with the ConceptNet knowledge graph, we added a biomedical QA dataset ([*MedQA-USMLE*](https://github.com/jind11/MedQA)) with a biomedical knowledge graph based on Disease Database and DrugBank. You can download all the data for this from [**[here]**](https://nlp.stanford.edu/projects/myasu/QAGNN/data_preprocessed_biomed.zip). Unzip it and put the `medqa_usmle` and `ddb` folders inside the `data/` directory. While this data is already preprocessed, we also provide the preprocessing scripts we used in `utils_biomed/`.
The resulting file structure will look like:
```plain
.
├── 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)
├── ...
├── obqa/
├── medqa_usmle/
└── ddb/
```
### 2. Train QA-GNN
For CommonsenseQA, run
```
./run_qagnn__csqa.sh
```
For OpenBookQA, run
```
./run_qagnn__obqa.sh
```
For MedQA-USMLE, run
```
./run_qagnn__medqa_usmle.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 questioExcerpt of 6,560 characters
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Michi Yasunaga · Stanford University
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matched fp:46c443897409fe34, topic:knowledge-graph, topic:question-answering
matched fp:46c443897409fe34, topic:language-model