---
title: "Fine-grained Fact Verification with Kernel Graph — Liu 2020"
description: "Automatic fact checking must decide whether a claim is supported, refuted, or lacks sufficient information, using sentences retrieved from Wikipedia."
authors: ["Zhenghao Liu", "Chenyan Xiong", "Maosong Sun", "Zhiyuan Liu"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.acl-main.655/"
doi: "10.18653/v1/2020.acl-main.655"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2020-fine-grained-fact-verification-kernel/"
---
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# Fine-grained Fact Verification with Kernel Graph Attention Network

**Authors:** Zhenghao Liu, [Chenyan Xiong](https://md.research.elamur.ai/free/en/authors/chenyan-xiong/), [Maosong Sun](https://md.research.elamur.ai/free/en/authors/maosong-sun/), [Zhiyuan Liu](https://md.research.elamur.ai/free/en/authors/zhiyuan-liu/) · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.acl-main.655/) · [PDF](https://aclanthology.org/2020.acl-main.655.pdf) · [DOI 10.18653/v1/2020.acl-main.655](https://doi.org/10.18653/v1/2020.acl-main.655) · [OpenAlex](https://openalex.org/W3016482365)

**Citations:** 183 citations (openalex-snapshot · 2026-10-14) · [99](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Automatic fact checking must decide whether a claim is supported, refuted, or lacks sufficient information, using sentences retrieved from Wikipedia. This is hard for two reasons. The retrieved evidence is noisy, and false claims are often plausible statements that differ from the truth only in subtle details.

The authors propose Kernel Graph Attention Network (KGAT). It treats every claim–evidence pair as a node in a fully connected graph and initialises each node with BERT. It then replaces the usual dot-product attention with Gaussian matching kernels in two places:
- **Edge kernels** choose which tokens from neighbouring nodes are passed along during multi-layer propagation.
- **Node kernels** weight how much each evidence node contributes to the final label.

On the FEVER benchmark, KGAT reaches a 70.38% FEVER score, ahead of earlier BERT- and GNN-based systems such as GEAR. The analyses show that kernel attention is much more concentrated than dot-product attention. This helps the model filter out noisy evidence and improves reasoning over several sentences. It also makes the model's decisions easier to interpret.

## Key points
- **Task and data:** FEVER, with three labels (SUPPORTS, REFUTES, NOT ENOUGH INFO) and Wikipedia as the evidence source. KGAT targets the claim verification step of the usual pipeline (document retrieval → sentence retrieval → verification).
- **Method:** The claim label probability is a sum over nodes. Each node's label prediction is weighted by its evidence-selection probability. The kernels operate on a cosine-similarity matrix built from BERT token representations.
- **Main result:** 70.38% FEVER score, with the clearest gains on claims that need several evidence pieces.
- **Evidence selection:** The sentence ranked first by node kernels covers nearly 80% of ground-truth evidence. GAT's node attention, by contrast, is close to uniform.
- **Code:** Released at github.com/thunlp/KernelGAT.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

99 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 93 not yet.

- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://md.research.elamur.ai/free/en/papers/diggelmann-2020-climate-fever-dataset-verification-real/)
- [A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/)
- [ARAFA: An LLM-Generated Arabic Fact-Checking Dataset](https://md.research.elamur.ai/free/en/papers/khalil-2026-arafa-llm-generated-arabic-fact/)
- [Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering](https://md.research.elamur.ai/free/en/papers/kim-2021-which-linguist-invented-lightbulb-presupposition/)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2020fine,
  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 the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.655},
  url = {https://doi.org/10.18653/v1/2020.acl-main.655}
}
```

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