Academy & Industry Research Collaboration Center (AIRCC)

Volume 10, Number 14, November 2020

Injecting Event Knowledge into Pre-Trained Language Models for Event Extraction


Zining Yang, Siyu Zhan, Mengshu Hou, Xiaoyang Zeng and Hao Zhu, University of Electronic Science & Technology of China, China


The recent pre-trained language model has made great success in many NLP tasks. In this paper, we propose an event extraction system based on the novel pre-trained language model BERT to extract both event trigger and argument. As a deep-learningbased method, the size of the training dataset has a crucial impact on performance. To address the lacking training data problem for event extraction, we further train the pretrained language model with a carefully constructed in-domain corpus to inject event knowledge to our event extraction system with minimal efforts. Empirical evaluation on the ACE2005 dataset shows that injecting event knowledge can significantly improve the performance of event extraction.


Natural Language Processing, Event Extraction, BERT, Lacking Training Data Problem.