Abstract: Event extraction aims to extract structured information automatically from massive unstructured texts to help people quickly understand the latest developments of events.Traditional methods are mainly implemented by classification or sequence labeling methods,which rely on a large amount of labeled data to train the model.In recent years,researchers have proposed to use machinereading comprehension models for event extraction,and through task conversion and combined use of machine rea-ding comprehension datasets for training to effectively alleviate the issue of insufficient annotation data.However,existing methods are limited to a single round of question answering and lack dependencies between different question and answer rounds.In addition,existing methods do not fully utilize entity knowledge in sentences.To this end,a new machine reading comprehension model for event extraction is proposed,and we extend existing methods in two ways.Firstly,by explicitly adding entity tag information in the sentence,making the model effectively learn the prior knowledge of the entities in the input sentence.Secondly,a historical conversational information encoding module is designed,and the attention mechanism is utilized to select important information from historical conversations to assist in inference.Finally,experiment results on a public dataset show that the new model achieves better performance than the existing methods based on the machine reading comprehension model.

Key words: Event extraction, Conversational machine reading comprehension, Entity information marking, Historical conversational information encoding, Attention mechanism