Event Causality Identification via Multi-template Prompt Tuning and Knowledge Enhancement
Event causality identification(ECI)is to aimed at identifying causal relationships between events in text.Most of the existing methods are based on fine-tuning paradigm,which cannot fully exploit the pre-trained language to capture the cues of implicit causality.This paper proposes an event causality identification method based on multi-template cue tuning and knowledge enhancement.A unique total cue template is designed for the ECI task,with dif-ferent seed cue templates for explicit and implicit event causality.All cue templates are integrated and trained a cue tuning approach adapted to the ECI task.By introducing external knowledge bases such as ConceptNet and Oxford Dictionaries,different knowledge of events and event relations is integrated into the cue templates to strengthen im-plicit causality cues.Experimental results on EventStoryLine and Causal-TimeBank show that our approach outper-forms existing methods.
万方数据
维普
