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        <title>Event Extraction by Answering (Almost) Natural Questions</title>
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        <namePart type="given">Xinya</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <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 (i.e., in a zero-shot learning setting).</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.49</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Event Extraction by Answering (Almost) Natural Questions
%A Du, Xinya
%A Cardie, Claire
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F du-cardie-2020-event
%X 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 (i.e., in a zero-shot learning setting).
%R 10.18653/v1/2020.emnlp-main.49
%U https://aclanthology.org/2020.emnlp-main.49
%U https://doi.org/10.18653/v1/2020.emnlp-main.49
%P 671-683

Markdown (Informal)

[Event Extraction by Answering (Almost) Natural Questions](https://aclanthology.org/2020.emnlp-main.49) (Du & Cardie, EMNLP 2020)

• Event Extraction by Answering (Almost) Natural Questions (Du & Cardie, EMNLP 2020)

ACL

• Xinya Du and Claire Cardie. 2020. Event Extraction by Answering (Almost) Natural Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 671–683, Online. Association for Computational Linguistics.