ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction
Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan
Abstract
Current state-of-the-art systems for joint entity relation extraction However, annotations for these additional tasks such as coreference resolution and event extraction are always equally hard (or even harder) to obtain. In this work, we propose a pre-training method ENPAR to improve the joint extraction performance. EN-PAR requires only the additional entity annotations that are much easier to collect. Unlike most existing works that only consider incorporating entity information into the sentence encoder, we further utilize the entity pair information. Specifically, we devise four novel objectives, i.e., masked entity typing, masked entity prediction, adversarial context discrimination, and permutation prediction, to pretrain an entity encoder and an entity pair encoder. Comprehensive experiments show that the proposed pre-training method achieves significant improvement over BERT on ACE05, SciERC, and NYT, and outperforms current state-of-the-art on ACE05.