There are source codes for Fine-grained Fact Verification with Kernel Graph Attention Network.

For more information about the FEVER 1.0 shared task can be found on this website.

Fact Extraction and Verification with SCIFACT

The shared task introduces scientific claim verification for helping scientists, clinicians, and public to verify the credibility of such claims with scientific literature, especially for the claims related to COVID-19.
>> Reproduce Our Results >> About SCIFACT Dataset >> Our Paper

• Python 3.X
• fever_score
• Pytorch
• pytorch_pretrained_bert
• transformers

• All data and BERT based chechpoints can be found at Ali Drive.
• RoBERTa based models and chechpoints can be found at Ali Drive.

• BERT based ranker.
• Go to the retrieval_model folder for more information.

• Pre-train BERT with claim-evidence pairs.
• Go to the pretrain folder for more information.

• Our KGAT model.
• Go to the kgat folder for more information.

The results are all on Codalab leaderboard.

KGAT performance with different pre-trained language model.

@inproceedings{liu2020kernel,
  title={Fine-grained Fact Verification with Kernel Graph Attention Network},
  author={Liu, Zhenghao and Xiong, Chenyan and Sun, Maosong and Liu, Zhiyuan},
  booktitle={Proceedings of ACL},
  year={2020}
}

@inproceedings{liu2020adapting,
    title = {Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling},
    author = {Liu, Zhenghao and Xiong, Chenyan and Dai, Zhuyun and Sun, Si and Sun, Maosong and Liu, Zhiyuan},
    booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
   year={2020}
}

If you have questions, suggestions and bug reports, please email:

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