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    <titleInfo>
        <title>Unsupervised Keyword Extraction for Full-Sentence VQA</title>
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    <name type="personal">
        <namePart type="given">Kohei</namePart>
        <namePart type="family">Uehara</namePart>
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    <name type="personal">
        <namePart type="given">Tatsuya</namePart>
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            <title>Proceedings of the First International Workshop on Natural Language Processing Beyond Text</title>
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            <namePart type="given">Giuseppe</namePart>
            <namePart type="family">Castellucci</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Simone</namePart>
            <namePart type="family">Filice</namePart>
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        <name type="personal">
            <namePart type="given">Soujanya</namePart>
            <namePart type="family">Poria</namePart>
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            <namePart type="given">Erik</namePart>
            <namePart type="family">Cambria</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during dataset construction. This study envisions a VQA task for natural situations, where the answers are more likely to be sentences rather than single words. To bridge the gap between this natural VQA and existing VQA approaches, a novel unsupervised keyword extraction method is proposed. The method is based on the principle that the full-sentence answers can be decomposed into two parts: one that contains new information answering the question (i.e. keywords), and one that contains information already included in the question. Discriminative decoders were designed to achieve such decomposition, and the method was experimentally implemented on VQA datasets containing full-sentence answers. The results show that the proposed model can accurately extract the keywords without being given explicit annotations describing them.</abstract>
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    <identifier type="doi">10.18653/v1/2020.nlpbt-1.6</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Unsupervised Keyword Extraction for Full-Sentence VQA
%A Uehara, Kohei
%A Harada, Tatsuya
%Y Castellucci, Giuseppe
%Y Filice, Simone
%Y Poria, Soujanya
%Y Cambria, Erik
%Y Specia, Lucia
%S Proceedings of the First International Workshop on Natural Language Processing Beyond Text
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F uehara-harada-2020-unsupervised
%X In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during dataset construction. This study envisions a VQA task for natural situations, where the answers are more likely to be sentences rather than single words. To bridge the gap between this natural VQA and existing VQA approaches, a novel unsupervised keyword extraction method is proposed. The method is based on the principle that the full-sentence answers can be decomposed into two parts: one that contains new information answering the question (i.e. keywords), and one that contains information already included in the question. Discriminative decoders were designed to achieve such decomposition, and the method was experimentally implemented on VQA datasets containing full-sentence answers. The results show that the proposed model can accurately extract the keywords without being given explicit annotations describing them.
%R 10.18653/v1/2020.nlpbt-1.6
%U https://aclanthology.org/2020.nlpbt-1.6/
%U https://doi.org/10.18653/v1/2020.nlpbt-1.6
%P 51-59

Markdown (Informal)

[Unsupervised Keyword Extraction for Full-Sentence VQA](https://aclanthology.org/2020.nlpbt-1.6/) (Uehara & Harada, nlpbt 2020)

• Unsupervised Keyword Extraction for Full-Sentence VQA (Uehara & Harada, nlpbt 2020)

ACL

• Kohei Uehara and Tatsuya Harada. 2020. Unsupervised Keyword Extraction for Full-Sentence VQA. In Proceedings of the First International Workshop on Natural Language Processing Beyond Text, pages 51–59, Online. Association for Computational Linguistics.