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        <title>SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification</title>
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    <abstract>This study proposes Semantic-Infused SElective Graph Reasoning (SISER) for fact verification, which newly presents semantic-level graph reasoning and injects its reasoning-enhanced representation into other types of graph-based and sequence-based reasoning methods. SISER combines three reasoning types: 1) semantic-level graph reasoning, which uses a semantic graph from evidence sentences, whose nodes are elements of a triple – \textlessSubject, Verb, Object\textgreater, 2) “semantic-infused” sentence-level “selective” graph reasoning, which combine semantic-level and sentence-level representations and perform graph reasoning in a selective manner using the node selection mechanism, and 3) sequence reasoning, which concatenates all evidence sentences and performs attention-based reasoning. Experiment results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that SISER outperforms the previous graph-based approaches and achieves state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification
%A Park, Eunhwan
%A Lee, Jong-Hyeon
%A Jeon, DongHyeon
%A Kim, Seonhoon
%A Kang, Inho
%A Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F park-etal-2022-siser
%X This study proposes Semantic-Infused SElective Graph Reasoning (SISER) for fact verification, which newly presents semantic-level graph reasoning and injects its reasoning-enhanced representation into other types of graph-based and sequence-based reasoning methods. SISER combines three reasoning types: 1) semantic-level graph reasoning, which uses a semantic graph from evidence sentences, whose nodes are elements of a triple – \textlessSubject, Verb, Object\textgreater, 2) “semantic-infused” sentence-level “selective” graph reasoning, which combine semantic-level and sentence-level representations and perform graph reasoning in a selective manner using the node selection mechanism, and 3) sequence reasoning, which concatenates all evidence sentences and performs attention-based reasoning. Experiment results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that SISER outperforms the previous graph-based approaches and achieves state-of-the-art performance.
%U https://aclanthology.org/2022.coling-1.117
%P 1367-1378

Markdown (Informal)

[SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification](https://aclanthology.org/2022.coling-1.117) (Park et al., COLING 2022)

• SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification (Park et al., COLING 2022)

ACL

• Eunhwan Park, Jong-Hyeon Lee, DongHyeon Jeon, Seonhoon Kim, Inho Kang, and Seung-Hoon Na. 2022. SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1367–1378, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.