Abstract: Event factuality is the description of the real situation of events in text.It is the basic task of many related applications in the field of natural language processing.At present,most researches on event factuality use labeled events to identify event factuality,which is inconvenient for practical application,and ignores the impact of different event sources on event factuality.Aiming at the existing problems,an end-to-end joint model JESF for event factuality identification is proposed.The model can carry out three tasks at the same time:event identification,event source identification and event factuality identification.Using bidirectional encoder representations from transformers(BERT)and linguistic features to strengthen the semantic representation of words.Graph convolutional network(GCN) is constructed by using attention mechanism and dependency syntax tree to effectively extract semantic and syntactic features.In particular,the model can also be applied to the task of event factuality considering only the default source(text author).Experimental results on FactBank,Meantime,UW and UDS-IH2 show that the proposed model is better than the benchmark model.

Key words: Event factuality identification, End-to-End, Joint model, Graph convolutional network, Bidirectional encoder representations from transformers