Trigger is Non-central: Jointly event extraction via label-aware representations with multi-task learning

Abstract

Event extraction (EE) occupies an important position in information extraction. Recently, deep neural network methods have been demonstrated to learn potential features well. However, existing networks for EE suffer from the following challenges: (1) Event argument extraction relies heavily on the classification of event triggers. (2) Previous works fail to exploit the predefined label representations for EE. (3) The interactive information between candidate arguments has not been fully exploited. To address the above mentioned problems, in this paper, we propose an advanced multi-task learning framework, named TNC, based on a fresh concept proposed by us: Trigger is Non-Central, in which event argument extraction no longer depends on the event triggers but is performed synchronously with it. Our TNC extracts multiple event triggers and arguments simultaneously by adopting label representations and an auxiliary task, named Sentence Event Identification (SEI), which is devised to extract the event types contained in a sentence. In addition, we design a special symbol to merge the representation of candidate arguments over the Transformer encoder. We experiment on the widely used ACE 2005 corpora and TAC-KBP 2015, and the experimental results have proved that our model achieves state-of-the-art compared to other models, with higher effectiveness and adaptability.