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    <titleInfo>
        <title>Question Answering Using Hierarchical Attention on Top of BERT Features</title>
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    <name type="personal">
        <namePart type="given">Reham</namePart>
        <namePart type="family">Osama</namePart>
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    <name type="personal">
        <namePart type="given">Nagwa</namePart>
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    <name type="personal">
        <namePart type="given">Marwan</namePart>
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            <title>Proceedings of the 2nd Workshop on Machine Reading for Question Answering</title>
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        <name type="personal">
            <namePart type="given">Adam</namePart>
            <namePart type="family">Fisch</namePart>
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        <name type="personal">
            <namePart type="given">Alon</namePart>
            <namePart type="family">Talmor</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Robin</namePart>
            <namePart type="family">Jia</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Minjoon</namePart>
            <namePart type="family">Seo</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Danqi</namePart>
            <namePart type="family">Chen</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>The model submitted works as follows. When supplied a question and a passage it makes use of the BERT embedding along with the hierarchical attention model which consists of 2 parts, the co-attention and the self-attention, to locate a continuous span of the passage that is the answer to the question.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5825</identifier>
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        <url>https://aclanthology.org/D19-5825</url>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Question Answering Using Hierarchical Attention on Top of BERT Features
%A Osama, Reham
%A El-Makky, Nagwa
%A Torki, Marwan
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F osama-etal-2019-question
%X The model submitted works as follows. When supplied a question and a passage it makes use of the BERT embedding along with the hierarchical attention model which consists of 2 parts, the co-attention and the self-attention, to locate a continuous span of the passage that is the answer to the question.
%R 10.18653/v1/D19-5825
%U https://aclanthology.org/D19-5825
%U https://doi.org/10.18653/v1/D19-5825
%P 191-195

Markdown (Informal)

[Question Answering Using Hierarchical Attention on Top of BERT Features](https://aclanthology.org/D19-5825) (Osama et al., 2019)

• Question Answering Using Hierarchical Attention on Top of BERT Features (Osama et al., 2019)

ACL

• Reham Osama, Nagwa El-Makky, and Marwan Torki. 2019. Question Answering Using Hierarchical Attention on Top of BERT Features. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 191–195, Hong Kong, China. Association for Computational Linguistics.