Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension
This work addresses the labor-intensive annotation bottleneck in event detection for NLP researchers and practitioners, offering an incremental improvement over existing methods.
The authors tackled the problem of sentence-level event detection by eliminating the need for trigger word annotation, proposing a trigger-free model that uses machine reading comprehension and prompt learning. Their approach achieved competitive performance on ACE2005 and MAVEN benchmark datasets.
The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words are used to classify event types from sentences. However, trigger classification highly depends on abundant annotated trigger words and the accuracy of trigger identification. In a real scenario, annotating trigger words is time-consuming and laborious. For this reason, we propose a trigger-free event detection model, which transforms event detection into a two-tower model based on machine reading comprehension and prompt learning. Compared to existing trigger-based and trigger-free methods, experimental studies on two event detection benchmark datasets (ACE2005 and MAVEN) have shown that the proposed approach can achieve competitive performance.