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    <titleInfo>
        <title>Addressing the Winograd Schema Challenge as a Sequence Ranking Task</title>
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    <name type="personal">
        <namePart type="given">Juri</namePart>
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    <name type="personal">
        <namePart type="given">Anette</namePart>
        <namePart type="family">Frank</namePart>
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            <title>Proceedings of the First International Workshop on Language Cognition and Computational Models</title>
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        <name type="personal">
            <namePart type="given">Manjira</namePart>
            <namePart type="family">Sinha</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Tirthankar</namePart>
            <namePart type="family">Dasgupta</namePart>
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    <abstract>The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.</abstract>
    <identifier type="citekey">opitz-frank-2018-addressing</identifier>
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        <url>https://aclanthology.org/W18-4105</url>
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    <part>
        <date>2018-08</date>
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            <start>41</start>
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%0 Conference Proceedings
%T Addressing the Winograd Schema Challenge as a Sequence Ranking Task
%A Opitz, Juri
%A Frank, Anette
%Y Sinha, Manjira
%Y Dasgupta, Tirthankar
%S Proceedings of the First International Workshop on Language Cognition and Computational Models
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F opitz-frank-2018-addressing
%X The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.
%U https://aclanthology.org/W18-4105
%P 41-52

Markdown (Informal)

[Addressing the Winograd Schema Challenge as a Sequence Ranking Task](https://aclanthology.org/W18-4105) (Opitz & Frank, LCCM 2018)

• Addressing the Winograd Schema Challenge as a Sequence Ranking Task (Opitz & Frank, LCCM 2018)

ACL

• Juri Opitz and Anette Frank. 2018. Addressing the Winograd Schema Challenge as a Sequence Ranking Task. In Proceedings of the First International Workshop on Language Cognition and Computational Models, pages 41–52, Santa Fe, New Mexico, USA. Association for Computational Linguistics.