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

• URL: http://arxiv.org/abs/2310.20236v1
• Date: Tue, 31 Oct 2023 07:41:24 GMT
• Title: Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
• Authors: Fei Cheng, Masayuki Asahara, Ichiro Kobayashi, Sadao Kurohashi
• Abstract summary: Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (Tlink) between two mentions. This paper presents an event centric model that allows to manage dynamic event representations across multiple Tlink categories. Our proposal outperforms state-of-the-art models and two transfer learning baselines on both the English and Japanese data.
• Score: 35.27714529976667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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