Document-Level Event Argument Extraction by Conditional Generation

• URL: http://arxiv.org/abs/2104.05919v1
• Date: Tue, 13 Apr 2021 03:36:38 GMT
• Title: Document-Level Event Argument Extraction by Conditional Generation
• Authors: Sha Li, Heng Ji, Jiawei Han
• Abstract summary: Event extraction has long been treated as a sentence-level task in the IE community. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents.
• Score: 75.73327502536938
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human information-seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WikiEvents datasets respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model's trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE.

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