Kyoto University
National Institute for Japanese Language and Linguistics
Ochanomizu University
Kyoto University
抄録(英)
Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) 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 transfer learning baselines on both the English and Japanese data.
出版者
Association for Computational Linguistics
雑誌名
Findings of the Association for Computational Linguistics: EMNLP 2020
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
2020.findings-emnlp.121.pdf
(500.63KB)
運用指針，オープンアクセス方針，等
