# DEKCOR-CommonsenseQA

**Repository Path**: wandehua/DEKCOR-CommonsenseQA

## Basic Information

- **Project Name**: DEKCOR-CommonsenseQA
- **Description**: 截至2021.11.17，CommonsenseQA Leadboard 上得分最高的论文模型代码
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-17
- **Last Updated**: 2022-05-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Fusing Context Into Knowledge Graph for Commonsense Question Answering

This PyTorch package implements the DEKCOR model for Commonsense Question Answering, as described in:

Yichong Xu∗, Chenguang Zhu∗, Ruochen Xu, Yang Liu, Michael Zeng and Xuedong Huang<br/>
[Fusing Context Into Knowledge Graph for Commonsense Question Answering](https://aclanthology.org/2021.findings-acl.102.pdf)</br>
Findings of The 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021<br/>
[arXiv version](https://arxiv.org/pdf/2012.04808.pdf)

Please cite the above paper if you use this code. 

## Results
This package achieves the state-of-art performance of 80.7% (single model), 83.3% (ensemble) on the [CommonsenseQA leaderboard](https://www.tau-nlp.org/csqa-leaderboard).

## Quickstart 

1. pull docker: </br>
   ```> docker pull yichongx/csqa:acl2021```

2. run docker </br>
   ```> nvidia-docker run -it --mount src='/',target=/workspace/,type=bind yichongx/csqa:acl2021 /bin/bash``` </br>
    Please refer to the following link if you first use docker: https://docs.docker.com/

## Use the data
Pre-processed data is located at ```data/```.

## Use the code
1. train a model
   > bash bash/task_train.sh
2. make prediction
   > bash bash/task_predict.sh

## Notes and Acknowledgments
The code is developed based on KCR: https://github.com/jessionlin/csqa

by Yichong Xu</br>
yicxu@microsoft.com

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
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