Targeted BERT Pre-training and Fine-Tuning Approach for Entity Relation Extraction

Entity relation extraction (ERE) is an important task in the field of information extraction. With the wide application of pre-training language model (PLM) in natural language processing (NLP), using PLM has become a brand new research direction of ERE. In this paper, BERT is used to extracting entity-relations, and a separated pipeline architecture is proposed. ERE was decomposed into entity-relation classification sub-task and entity-pair annotation sub-task. Both sub-tasks conduct the pre-training and fine-tuning independently. Combining dynamic and static masking, new Verb-MLM and Entity-MLM BERT pre-training tasks were put forward to enhance the correlation between BERT pre-training and Targeted NLP downstream task-ERE. Inter-layer sharing attention mechanism was added to the model, sharing the attention parameters according to the similarity of the attention matrix. Contrast experiment on the SemEavl 2010 Task8 dataset demonstrates that the new MLM task and inter-layer sharing attention mechanism improve the performance of BERT on the entity relation extraction effectively.