DCT-Centered Temporal Relation Extraction

Liang Wang, Peifeng Li, Sheng Xu


Abstract
Most previous work on temporal relation extraction only focused on extracting the temporal relations among events or suffered from the issue of different expressions of events, timexes and Document Creation Time (DCT). Moreover, DCT can act as a hub to semantically connect the other events and timexes in a document. Unfortunately, previous work cannot benefit from such critical information. To address the above issues, we propose a unified DCT-centered Temporal Relation Extraction model DTRE to identify the relations among events, timexes and DCT. Specifically, sentence-style DCT representation is introduced to address the first issue and unify event expressions, timexes and DCT. Then, a DCT-aware graph is applied to obtain their contextual structural representations. Furthermore, a DCT-anchoring multi-task learning framework is proposed to jointly predict three types of temporal relations in a batch. Finally, we apply a DCT-guided global inference to further enhance the global consistency among different relations. Experimental results on three datasets show that our DTRE outperforms several SOTA baselines on E-E, E-T and E-D significantly.
Anthology ID:
2022.coling-1.182
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:
2087–2097
Language:
URL:
https://aclanthology.org/2022.coling-1.182
DOI:
Bibkey:
Cite (ACL):
Liang Wang, Peifeng Li, and Sheng Xu. 2022. DCT-Centered Temporal Relation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2087–2097, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
DCT-Centered Temporal Relation Extraction (Wang et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.182.pdf