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

Autor:	Cheng, Fei, Asahara, Masayuki, Kobayashi, Ichiro, Kurohashi, Sadao
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: EMNLP 2020 Findings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.20236 Zobrazit plný text záznamu View this record from Arxiv