Dynamically Updating Event Representations for Temporal Relation
  Classification with Multi-category Learning
Fei Cheng Masayuki Asahara Ichiro Kobayashi Sadao Kurohashi
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.