Efficient End-to-end Learning of Cross-event Dependencies for Document-level Event Extraction
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Document-level event extraction is important for indexing the most important information in a document to facilitate downstream tasks such as information retrieval or question answering. However, it is a challenging task because it requires the understanding of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction generally confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based model on ACE05, while enjoys significantly higher efficiency.
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