Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
This work improves temporal relation classification for natural language processing applications, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of temporal relation classification by addressing limitations in sharing information between temporal links and using independent classifiers, proposing an event-centric model with multi-task learning. The model outperforms state-of-the-art models and transfer learning baselines on English and Japanese datasets.
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.