Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning

Fei Cheng, Masayuki Asahara, Ichiro Kobayashi, Sadao Kurohashi


Abstract
Temporal relation classification is the pair-wise task for identifying the relation of a temporal link (TLINKs) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) hinder from using the whole data. This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. Our model deals with three TLINK categories with multi-task learning to leverage the full size of data. The experimental results show that our proposal outperforms state-of-the-art models and two strong transfer learning baselines on both the English and Japanese data.
Anthology ID:
2020.findings-emnlp.121
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1352–1357
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.121
DOI:
10.18653/v1/2020.findings-emnlp.121
Bibkey:
Cite (ACL):
Fei Cheng, Masayuki Asahara, Ichiro Kobayashi, and Sadao Kurohashi. 2020. Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1352–1357, Online. Association for Computational Linguistics.
Cite (Informal):
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning (Cheng et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.121.pdf