GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction
Jiarun Cao, Sophia Ananiadou · 2021
Previous neural seq2seq models have shown the effectiveness for jointly extracting relation triplets.However, most of these models suffer from incompletion and disorder problems when they extract multi-token entities from input sentences.To tackle these problems, we propose a generative, multi-task learning framework, named GenerativeRE.We firstly propose a special entity labelling method on both input and output sequences.During the training stage, GenerativeRE fine-tunes the pretrained generative model and learns the special entity labels simultaneously.During the inference stage, we propose a novel copy mechanism equipped with three mask strategies, to generate the most probable tokens by diminishing the scope of the model decoder.Experimental results show that our model achieves 4.6% and 0.9% F1 score improvements over the current state-of-the-art methods in the NYT24 and NYT29 benchmark datasets respectively.