End-to-End Event Factuality Identification with Cross-Lingual Information

Abstract

Event factuality is a description of the real situation of events in text. Event Factuality Identification (EFI) is the basic task of many related applications in the field of natural language processing. At present, most studies about EFI are carried out with the annotated event mentions, which is not applicable for practical application, and ignores the opinion of different event sources on event factuality. Moreover, previous work did not use cross-lingual information for EFI. We propose an end-to-end joint model JESF, which uses Bert to encode sentences and uses lingual feature to enrich the semantic representation of sentences, and then use BiLSTM to capture the serialized semantic features of sentences; Then, the multi-head attention is used to learn the event characteristics and identify the event mentions; After that, use multi-head attention to identify the event source; Finally, GCNs is used to capture the syntactic and semantic features, mult-head attention is used to capture the semantic features of sentences, event and event source features are integrated to identify event factuality. Especially, we use different cross-lingual related methods to learn supplementary sematic features from aligned Chinese sentences. The experimental results on FactBank show that JESF is effective and the Chinese information is helpful for English EFI, and the more effective method is to use Chinese cue as features for EFI.