Multi-Role Event Argument Extraction as Machine Reading Comprehension with Argument Match Optimization

Extracting arguments for the pre-defined roles is a crucial step for event extraction. Recently, there are some insightful works that view it as a machine reading comprehension problem and achieve significant progress. However, most of them need multi-turns to extract the arguments of each role independently, which ignores the relationships among roles in the same event. To alleviate this problem, we propose a novel Multi-Role Argument Extraction method named MRAE which can exploit the relationship of event roles by extracting all arguments for an event simultaneously. To force MRAE to locate more arguments accurately, we propose an argument match optimization loss based on the minimum risk training to exploit sentence-level F1 score. We conduct experiments on the widely used ACE2005 dataset. The experimental results demonstrate that MRAE outperforms the competitor methods by at least +1.2% F1 score on argument extraction, and also shows superiority on data scarce scenarios.