CosG: A Graph-Based Contrastive Learning Method for Fact Verification
Fact verification aims to verify the authenticity of a given claim based on the retrieved evidence from Wikipedia articles. Existing works mainly focus on enhancing the semantic representation of evidence, e.g., introducing the graph structure to model the evidence relation. However, previous method...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/10/3471 |
| _version_ | 1797533918391435264 |
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| author | Chonghao Chen Jianming Zheng Honghui Chen |
| author_facet | Chonghao Chen Jianming Zheng Honghui Chen |
| author_sort | Chonghao Chen |
| collection | DOAJ |
| description | Fact verification aims to verify the authenticity of a given claim based on the retrieved evidence from Wikipedia articles. Existing works mainly focus on enhancing the semantic representation of evidence, e.g., introducing the graph structure to model the evidence relation. However, previous methods can’t well distinguish semantic-similar claims and evidences with distinct authenticity labels. In addition, the performances of graph-based models are limited by the over-smoothing problem of graph neural networks. To this end, we propose a graph-based contrastive learning method for fact verification abbreviated as CosG, which introduces a contrastive label-supervised task to help the encoder learn the discriminative representations for different-label claim-evidence pairs, as well as an unsupervised graph-contrast task, to alleviate the unique node features loss in the graph propagation. We conduct experiments on FEVER, a large benchmark dataset for fact verification. Experimental results show the superiority of our proposal against comparable baselines, especially for the claims that need multiple-evidences to verify. In addition, CosG presents better model robustness on the low-resource scenario. |
| first_indexed | 2024-03-10T11:22:29Z |
| format | Article |
| id | doaj.art-edc41ac411d948b3a280e3d098cc515c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:22:29Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-edc41ac411d948b3a280e3d098cc515c2023-11-21T19:58:53ZengMDPI AGSensors1424-82202021-05-012110347110.3390/s21103471CosG: A Graph-Based Contrastive Learning Method for Fact VerificationChonghao Chen0Jianming Zheng1Honghui Chen2Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, ChinaScience and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, ChinaFact verification aims to verify the authenticity of a given claim based on the retrieved evidence from Wikipedia articles. Existing works mainly focus on enhancing the semantic representation of evidence, e.g., introducing the graph structure to model the evidence relation. However, previous methods can’t well distinguish semantic-similar claims and evidences with distinct authenticity labels. In addition, the performances of graph-based models are limited by the over-smoothing problem of graph neural networks. To this end, we propose a graph-based contrastive learning method for fact verification abbreviated as CosG, which introduces a contrastive label-supervised task to help the encoder learn the discriminative representations for different-label claim-evidence pairs, as well as an unsupervised graph-contrast task, to alleviate the unique node features loss in the graph propagation. We conduct experiments on FEVER, a large benchmark dataset for fact verification. Experimental results show the superiority of our proposal against comparable baselines, especially for the claims that need multiple-evidences to verify. In addition, CosG presents better model robustness on the low-resource scenario.https://www.mdpi.com/1424-8220/21/10/3471contrastive learningfact verificationentity graphgraph neural network |
| spellingShingle | Chonghao Chen Jianming Zheng Honghui Chen CosG: A Graph-Based Contrastive Learning Method for Fact Verification Sensors contrastive learning fact verification entity graph graph neural network |
| title | CosG: A Graph-Based Contrastive Learning Method for Fact Verification |
| title_full | CosG: A Graph-Based Contrastive Learning Method for Fact Verification |
| title_fullStr | CosG: A Graph-Based Contrastive Learning Method for Fact Verification |
| title_full_unstemmed | CosG: A Graph-Based Contrastive Learning Method for Fact Verification |
| title_short | CosG: A Graph-Based Contrastive Learning Method for Fact Verification |
| title_sort | cosg a graph based contrastive learning method for fact verification |
| topic | contrastive learning fact verification entity graph graph neural network |
| url | https://www.mdpi.com/1424-8220/21/10/3471 |
| work_keys_str_mv | AT chonghaochen cosgagraphbasedcontrastivelearningmethodforfactverification AT jianmingzheng cosgagraphbasedcontrastivelearningmethodforfactverification AT honghuichen cosgagraphbasedcontrastivelearningmethodforfactverification |