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    <titleInfo>
        <title>♫ MuSiQue: Multihop Questions via Single-hop Question Composition</title>
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        <namePart type="given">Harsh</namePart>
        <namePart type="family">Trivedi</namePart>
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    <name type="personal">
        <namePart type="given">Niranjan</namePart>
        <namePart type="family">Balasubramanian</namePart>
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    <name type="personal">
        <namePart type="given">Tushar</namePart>
        <namePart type="family">Khot</namePart>
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    <name type="personal">
        <namePart type="given">Ashish</namePart>
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    <abstract>Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts. Can we create a question answering (QA) dataset that, by construction, requires proper multihop reasoning? To this end, we introduce a bottom–up approach that systematically selects composable pairs of single-hop questions that are connected, that is, where one reasoning step critically relies on information from another. This bottom–up methodology lets us explore a vast space of questions and add stringent filters as well as other mechanisms targeting connected reasoning. It provides fine-grained control over the construction process and the properties of the resulting k-hop questions. We use this methodology to create MuSiQue-Ans, a new multihop QA dataset with 25K 2–4 hop questions. Relative to existing datasets, MuSiQue-Ans is more difficult overall (3\times increase in human–machine gap), and harder to cheat via disconnected reasoning (e.g., a single-hop model has a 30-point drop in F1). We further add unanswerable contrast questions to produce a more stringent dataset, MuSiQue-Full. We hope our datasets will help the NLP community develop models that perform genuine multihop reasoning.1</abstract>
    <identifier type="citekey">trivedi-etal-2022-musique</identifier>
    <identifier type="doi">10.1162/tacl_a_00475</identifier>
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        <url>https://aclanthology.org/2022.tacl-1.31</url>
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        <date>2022</date>
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            <start>539</start>
            <end>554</end>
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%0 Journal Article
%T ♫ MuSiQue: Multihop Questions via Single-hop Question Composition
%A Trivedi, Harsh
%A Balasubramanian, Niranjan
%A Khot, Tushar
%A Sabharwal, Ashish
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F trivedi-etal-2022-musique
%X Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts. Can we create a question answering (QA) dataset that, by construction, requires proper multihop reasoning? To this end, we introduce a bottom–up approach that systematically selects composable pairs of single-hop questions that are connected, that is, where one reasoning step critically relies on information from another. This bottom–up methodology lets us explore a vast space of questions and add stringent filters as well as other mechanisms targeting connected reasoning. It provides fine-grained control over the construction process and the properties of the resulting k-hop questions. We use this methodology to create MuSiQue-Ans, a new multihop QA dataset with 25K 2–4 hop questions. Relative to existing datasets, MuSiQue-Ans is more difficult overall (3\times increase in human–machine gap), and harder to cheat via disconnected reasoning (e.g., a single-hop model has a 30-point drop in F1). We further add unanswerable contrast questions to produce a more stringent dataset, MuSiQue-Full. We hope our datasets will help the NLP community develop models that perform genuine multihop reasoning.1
%R 10.1162/tacl_a_00475
%U https://aclanthology.org/2022.tacl-1.31
%U https://doi.org/10.1162/tacl_a_00475
%P 539-554

Markdown (Informal)

[♫ MuSiQue: Multihop Questions via Single-hop Question Composition](https://aclanthology.org/2022.tacl-1.31) (Trivedi et al., TACL 2022)

• ♫ MuSiQue: Multihop Questions via Single-hop Question Composition (Trivedi et al., TACL 2022)

ACL

• Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. ♫ MuSiQue: Multihop Questions via Single-hop Question Composition. Transactions of the Association for Computational Linguistics, 10:539–554.