danlou / DEKCOR-CommonsenseQA

Official code for paper "Fusing Context Into Knowledge Graph for Commonsense QuestionAnswering"

Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension

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
Fusing Context Into Knowledge Graph for Commonsense QuestionAnswering
The 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021
arXiv version

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.

Quickstart

1. pull docker:
   > docker pull yichongx/csqa:acl2021

2. run docker
   > nvidia-docker run -it --mount src='/',target=/workspace/,type=bind yichongx/csqa:acl2021 /bin/bash
   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
[e-mail]

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. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Trademarks

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