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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- 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
Export citation
@inproceedings{park-etal-2022-siser,
title = "{SISER}: Semantic-Infused Selective Graph Reasoning for Fact Verification",
author = "Park, Eunhwan and
Lee, Jong-Hyeon and
Jeon, DongHyeon and
Kim, Seonhoon and
Kang, Inho and
Na, Seung-Hoon",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.117",
pages = "1367--1378",
abstract = "This study proposes \textbf{S}emantic-\textbf{I}nfused \textbf{SE}lective Graph \textbf{R}easoning (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) \textit{semantic}-level graph reasoning, which uses a semantic graph from evidence sentences, whose nodes are elements of a triple {--} {\textless}Subject, Verb, Object{\textgreater}, 2) {``}semantic-infused{''} \textit{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) \textit{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.",
}
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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 %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y 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.