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README.md

Kernel Graph Attention Network (KGAT)

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

model

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

Requirement

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

Data and Checkpoint

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

Retrieval Model

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

Pretrain Model

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

KGAT Model

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

Results

The results are all on Codalab leaderboard.

User Pre-train Model Label Accuracy FEVER Score
GEAR_single BERT (Base) 0.7160 0.6710
a.soleimani.b BERT (Large) 0.7186 0.6966
KGAT RoBERTa (Large) 0.7407 0.7038

KGAT performance with different pre-trained language model.

Pre-train Model Label Accuracy FEVER Score
BERT (Base) 0.7281 0.6940
BERT (Large) 0.7361 0.7024
RoBERTa (Large) 0.7407 0.7038
CorefBERT (RoBERT Large) 0.7596 0.7230

Citation

@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}
}

Contact

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

liu-zh16@mails.tsinghua.edu.cn

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

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