https://github.com/thunlp/kernelgat

The source codes for Fine-grained Fact Verification with Kernel Graph Attention Network.
https://github.com/thunlp/kernelgat

fact-verification graph-attention-network kernel

Last synced: 3 days ago
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The source codes for Fine-grained Fact Verification with Kernel Graph Attention Network.

• Host: GitHub
• URL: https://github.com/thunlp/kernelgat
• Owner: thunlp
• License: mit
• Created: 2019-10-22T05:32:42.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T10:24:37.000Z (almost 2 years ago)
• Last Synced: 2023-11-07T15:59:25.501Z (about 1 year ago)
• Topics: fact-verification, graph-attention-network, kernel
• Language: Python
• Homepage:
• Size: 2.26 MB
• Stars: 166
• Watchers: 9
• Forks: 36
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Kernel Graph Attention Network (KGAT)

There are source codes for [Fine-grained Fact Verification with Kernel Graph Attention Network](https://www.aclweb.org/anthology/2020.acl-main.655.pdf).

![model](https://github.com/thunlp/KernelGAT/blob/master/model.png)

For more information about the FEVER 1.0 shared task can be found on this [website](http://fever.ai).

## 😃 What's New

[Fact Extraction and Verification with SCIFACT](https://scifact.apps.allenai.org)

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](./scikgat) [>> About SCIFACT Dataset](https://www.aclweb.org/anthology/2020.emnlp-main.609.pdf) [>> Our Paper](https://www.aclweb.org/anthology/2020.findings-emnlp.216)

## 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](https://thunlp.oss-cn-qingdao.aliyuncs.com/KernelGAT/FEVER/KernelGAT.zip).

* RoBERTa based models and chechpoints can be found at [Ali Drive](https://thunlp.oss-cn-qingdao.aliyuncs.com/KernelGAT/FEVER/KernelGAT_roberta_large.zip).

## 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](https://competitions.codalab.org/competitions/18814#results).

| User | Pre-train Model| Label Accuracy| FEVER Score |

| -------- | -------- | --------  | --------  |

[GEAR_single](https://arxiv.org/pdf/1908.01843.pdf)|BERT \(Base\)|0\.7160|0\.6710|

|[a.soleimani.b](https://arxiv.org/pdf/1910.02655.pdf)|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](https://arxiv.org/abs/2004.06870) \(RoBERT Large\)|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}

}

```

```

@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}

}

```

## Contact

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

```

[email protected]

```