An Alleviate Exposure Bias Method in Joint Extraction of Entities and Relations

Joint extraction of entities and relations aims to discover entity mentions and relational facts simultaneously from unstructured texts, which is a critical step in knowledge graph construction, and serves as a basis of many high-level tasks in natural language processing. The joint extraction model gets more widespread attention as they can model the correlation between entity recognition and relation extraction more effectively. Most of the existing work uses a phased joint extraction method to deal with the problem of triple extraction in the text where there are multiple triples and entities overlapping at the same time, although reasonable performance improvement has been achieved, there are serious exposure bias problems. In this paper, we propose a novel method called fusional relation expression embedding (FREE) to tackle the exposure bias problem by fusing relation expression information. Besides, a novel feature fusion layer called conditional layer normalization is proposed to fuse prior information more effectively. We conduct a lot of comparative experiments on two widely used data sets. The in-depth analysis of the experimental results shows that the proposed method has significant advantages over the current state-of-the-art baseline model, and it can deal with various situations more effectively and achieve the competitive performance as the current advanced model for exposure bias problems without sacrificing efficiency.