RSGT: Relational Structure Guided Temporal Relation Extraction

Jie Zhou, Shenpo Dong, Hongkui Tu, Xiaodong Wang, Yong Dou


Abstract
Temporal relation extraction aims to extract temporal relations between event pairs, which is crucial for natural language understanding. Few efforts have been devoted to capturing the global features. In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational structure features that can fit for both inter-sentence and intra-sentence relations. Specifically, we construct a syntactic-and-semantic-based graph to extract relational structures. Then we present a graph neural network based model to learn the representation of this graph. After that, an auxiliary temporal neighbor prediction task is used to fine-tune the encoder to get more comprehensive node representations. Finally, we apply a conflict detection and correction algorithm to adjust the wrongly predicted labels. Experiments on two well-known datasets, MATRES and TB-Dense, demonstrate the superiority of our method (2.3% F1 improvement on MATRES, 3.5% F1 improvement on TB-Dense).
Anthology ID:
2022.coling-1.174
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:
2001–2010
Language:
URL:
https://aclanthology.org/2022.coling-1.174
DOI:
Bibkey:
Cite (ACL):
Jie Zhou, Shenpo Dong, Hongkui Tu, Xiaodong Wang, and Yong Dou. 2022. RSGT: Relational Structure Guided Temporal Relation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2001–2010, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
RSGT: Relational Structure Guided Temporal Relation Extraction (Zhou et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.174.pdf