Reasoning Over Semantic-Level Graph for Fact Checking
Open accessAbstract
Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence.In this work, we present a method suitable for reasoning about the semantic-level structure of evidence.Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling.We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet.Specifically, using XLNet as the backbone, we first utilize the graph structure to re-define the relative distances of words, with the intuition that semantically related words should have short distances.Then, we adopt graph convolutional network and graph attention network to propagate and aggregate information from neighboring nodes on the graph.We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.Our model is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score.
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Existing fact-verification approaches follow a multi-stage pipeline of claim detection, evidence retrieval and verdict prediction, require external databases, and can have considerable inference costs.
“Existing fact-verification approaches follow a multi-stage pipeline of claim detection, evidence retrieval and verdict prediction (Guo et al., 2022; Zhong et al., 2020). Such methods, however, require access to external databases and can have considerable inference costs.”
From SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023) · cites this paper as Zhong et al., 2020
Section 2 (page unavailable) describes a multi-stage pipeline that retrieves documents, selects evidence sentences, and predicts a verdict; Section 4.1 specifies Wikipedia documents as its evidence source. The pipeline takes a claim as input, so it does not establish claim detection as a stage. The passages do not assess inference costs. (LLM, conf=0.87)
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Monant Medical Misinformation Dataset
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A Multi-Level Attention Model for Evidence-Based Fact Checking
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RMLM: A Flexible Defense Framework for Proactively Mitigating Word-level Adversarial Attacks
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Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables
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Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction
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Fact-Driven Logical Reasoning for Machine Reading Comprehension
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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection
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Fake news detection for the Russian language
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Fact-checking Vietnamese Information Using Knowledge Graph, Datalog, and KG-BERT
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Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification
Kotonya, N., Spooner, T., Magazzeni, D., & Toni, F. (2021) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2021.fever-1.3 · Cited by 10 · see citing works
Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing
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Explainability of Automated Fact Verification Systems: A Comprehensive Review
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From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification
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Reasoning over Entity-Action-Location Graph for Procedural Text Understanding
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Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification
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- Authors
- Zhong, W., Xu, J., Tang, D., Xu, Z., Duan, N., Zhou, M., Wang, J., & Yin, J.
- Affiliations
- Sun Yat-sen University (CN)
- Peking University (CN)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 6170-6180
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3034808961
- Cited by
- 151
- References
- 36
- Citations by year
- 2026: 4 · 2025: 15 · 2024: 22 · 2023: 26 · 2022: 36 · 2021: 38 · 2020: 10
- FWCI
- 58.32
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (U1611264, U1711262, U1811261, U1811264, U1711261, U1911203)
- Basic and Applied Basic Research Foundation of Guangdong Province (2019B15 15130001)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- fact-checking, claim verification, semantic role labeling, graph convolutional networks, graph attention networks, XLNet, FEVER dataset, word distance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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