SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification

Eunhwan Park, Jong-Hyeon Lee, DongHyeon Jeon, Seonhoon Kim, Inho Kang, Seung-Hoon Na


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 – <Subject, Verb, Object>, 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.
Anthology ID:
2022.coling-1.117
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1367–1378
Language:
URL:
https://aclanthology.org/2022.coling-1.117
DOI:
Bibkey:
Cite (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.
Cite (Informal):
SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification (Park et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.117.pdf
Data
FEVER