MABERT: Mask-Attention-Based BERT for Chinese Event Extraction
Ling Ding, Xiaojun Chen, Jian Wei, Yang Xiang
Abstract
Event extraction is an essential but challenging task in information extraction. This task has considerably benefited from pre-trained language models, such as BERT. However, when it comes to the trigger-word mismatch problem in languages without natural delimiters, existing methods ignore the complement of lexical information to BERT. In addition, the inherent multi-role noise problem could limit the performance of methods when one sentence contains multiple events. In this article, we propose a Mask-Attention-based BERT (MABERT) framework for Chinese event extraction to address the above problems. Firstly, in order to avoid trigger-word mismatch and integrate lexical features into BERT layers directly, a mask-attention-based transformer augmented with two mask matrices is devised to replace the original one in BERT. By the mask-attention-based transformer, the character sequence interacts with external lexical semantics sufficiently and keeps its structure information at the same time. Moreover, against the multi-role noise problem, we make use of event type information from representation and classification, two aspects to enrich entity features, where type markers and event-schema-based mask matrix are proposed. Experimental results on the widely used ACE2005 dataset show the effectiveness of our proposed MABERT on Chinese event extraction task compared with other state-of-the-art methods.