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    <titleInfo>
        <title>A Nil-Aware Answer Extraction Framework for Question Answering</title>
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    <name type="personal">
        <namePart type="given">Souvik</namePart>
        <namePart type="family">Kundu</namePart>
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    <name type="personal">
        <namePart type="given">Hwee</namePart>
        <namePart type="given">Tou</namePart>
        <namePart type="family">Ng</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA). However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage. A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage. In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer. If the associated passage does not contain any valid answer, the QA system will correctly return Nil. We propose a novel nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step. We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an end-to-end fashion. Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer. Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.</abstract>
    <identifier type="citekey">kundu-ng-2018-nil</identifier>
    <identifier type="doi">10.18653/v1/D18-1456</identifier>
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        <url>https://aclanthology.org/D18-1456</url>
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    <part>
        <date>2018-oct-nov</date>
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            <start>4243</start>
            <end>4252</end>
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%0 Conference Proceedings
%T A Nil-Aware Answer Extraction Framework for Question Answering
%A Kundu, Souvik
%A Ng, Hwee Tou
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kundu-ng-2018-nil
%X Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA). However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage. A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage. In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer. If the associated passage does not contain any valid answer, the QA system will correctly return Nil. We propose a novel nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step. We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an end-to-end fashion. Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer. Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.
%R 10.18653/v1/D18-1456
%U https://aclanthology.org/D18-1456
%U https://doi.org/10.18653/v1/D18-1456
%P 4243-4252

Markdown (Informal)

[A Nil-Aware Answer Extraction Framework for Question Answering](https://aclanthology.org/D18-1456) (Kundu & Ng, EMNLP 2018)

• A Nil-Aware Answer Extraction Framework for Question Answering (Kundu & Ng, EMNLP 2018)

ACL

• Souvik Kundu and Hwee Tou Ng. 2018. A Nil-Aware Answer Extraction Framework for Question Answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4243–4252, Brussels, Belgium. Association for Computational Linguistics.