A multi-view heterogeneous and extractive graph attention network for evidential document-level event factuality identification

Evidential Document-level Event Factuality Identification (EvDEFI) aims to predict the factual nature of an event and extract evidential sentences from the document precisely. Previous work usually limited to only predicting the factuality of an event with respect to a document, and neglected the interpretability of the task. As a more fine-grained and interpretable task, EvDEFI is still in the early stage. The existing model only used shallow similarity calculation to extract evidences, and employed simple attentions without lexical features, which is quite coarse-grained. Therefore, we propose a novel EvDEFI model named Heterogeneous and Extractive Graph Attention Network (HEGAT), which can update representations of events and sentences by multi-view graph attentions based on tokens and various lexical features from both local and global levels. Experiments on EB-DEF-v2 corpus demonstrate that HEGAT model is superior to several competitive baselines and can validate the interpretability of the task.