Abstract: Most previous event coreference resolution models belong to pairwise similarity models,which judge whether the two events are coreferences by calculating the similarity between them.However,when two event mentions appear close to each other in the document,encoding one event contextual representation will introduce information from the other event,which degrades the performance of the model.To solve the problem,an end-to-end event coreference resolution method based on core sentence(ECR-CS) is proposed.The model automatically extracts event information and constructs a core sentence for each event mention according to the preset template,and uses the core sentence representation instead of the event representation.Since the core sentence contains only the information of a single event,the model can eliminate the interference of other event information when encoding the event representation.In addition,limited by the performance of event extraction,the core sentence may lose some important information of the event.The contextual representation of the event in the document is used to make up for this problem.To supplement the missing important information in the core sentence with the contextual information,a gated mechanism is introduced to filter the noise in the contextual representation.Experiments on dataset ACE2005 show that the CoNLL and AVG scores of ECR-CS improves by 1.76 and 1.04,respectively,compared with the state-of-the-art baseline model.

Key words: Event coreference resolution, Gated mechanism, Neural network, Pre-trained language models, Event core sentence