# qagnn

**Repository Path**: chenyang3817/qagnn

## Basic Information

- **Project Name**: qagnn
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: dev
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-03
- **Last Updated**: 2021-06-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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).
```
@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

- [Python](<https://www.python.org/>) == 3.7
- [PyTorch](<https://pytorch.org/get-started/locally/>) == 1.4.0
- [transformers](<https://github.com/huggingface/transformers/tree/v2.0.0>) == 2.0.0
- [torch-geometric](https://pytorch-geometric.readthedocs.io/) ==1.6.0

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 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:

```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)
        ├── ...
```

### 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!