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.

翻译：时序关系分类是一种成对任务，用于识别两个提及项——即事件、时间和文档创建时间（DCT）之间时序链接（TLINK）的关系。这一任务存在两个关键局限：1）涉及同一提及项的两个TLINK无法共享信息；2）现有模型为每个TLINK类别（E2E、E2T和E2D）采用独立分类器，阻碍了完整数据的利用。本文提出了一种以事件为中心的模型，能够在多个TLINK间管理动态事件表示。该模型通过多任务学习处理三种TLINK类别，从而充分利用完整数据集。实验结果表明，我们的方法在英语和日语数据上均优于当前最优模型及两种迁移学习基线。