Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies (2021)

Abstract: Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually 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 models on ACE05, while enjoys significantly higher computational efficiency.

## introduction narratives are account of a series of related events or experiences @xcite . extracting events in literature can help machines better understand the underlying narratives. a robust event extraction system is therefore crucial for fully understanding narratives. event extraction aims to identify events composed of a trigger of pre-defined types and the corresponding arguments from plain text @xcite . to gain full information about the extracted events, entity coreference and event coreference are important, as demonstrated in figure 1a . these two tasks require document-level modeling. the majority of the previous event extraction works focus on sentence level @xcite @xcite . some later works leverage document-level features, but still extract events at event coreference ## related works in this section, we summarize existing works on document-level information extraction and event extraction, and the application of structured prediction to event extraction tasks. ## proposed approach we develop a base model that makes independent predictions for each subtask under a multi-task ie framework. the proposed end-to-end framework, deed, then incorporates dvn into the base model to efficiently capture cross-event dependencies. ## analysis 6.1 performance of each component to understand the capabilities of each module, we show an evaluation breakdown on each component following previous works @xcite in as shown in ## conclusion in this paper, we investigate document-level event extraction that requires joint modeling of event and entity coreference. we propose a documentlevel event extraction framework, deed, which uses dvn to capture cross-event dependencies, and explore different end-to-end learning methods of dvn. experimental results show that deed achieves comparable performance to competitive baseline models, while deed is much favorable in terms of computation efficiency. we also found that incorporating noise into end-to-end dvn training procedure can result in higher dvn quality and better overall performance.