Event Extraction by Answering (Almost) Natural Questions

• URL: http://arxiv.org/abs/2004.13625v2
• Date: Thu, 4 Feb 2021 23:17:22 GMT
• Title: Event Extraction by Answering (Almost) Natural Questions
• Authors: Xinya Du and Claire Cardie
• Abstract summary: We introduce a new paradigm for event extraction by formulating it as a question answering (QA) task. Empirical results demonstrate that our framework outperforms prior methods substantially.
• Score: 40.13163091122463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argument extraction typically relies heavily on entity recognition as a preprocessing/concurrent step, causing the well-known problem of error propagation. To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task that extracts the event arguments in an end-to-end manner. Empirical results demonstrate that our framework outperforms prior methods substantially; in addition, it is capable of extracting event arguments for roles not seen at training time (zero-shot learning setting).

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