Event Extraction by Answering (Almost) Natural Questions (2020-04-28T00:00:00.000000Z)

TL;DR

This work introduces a new paradigm for event extraction by formulating it as a question answering (QA) task, which extracts the event arguments in an end-to-end manner and outperforms prior methods substantially.

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, which 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).

Authors

Claire Cardie

14 papers

Xinya Du

3 papers

TL;DR

Abstract

Authors

References41 items

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