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        <title>Hypothesis Only Baselines in Natural Language Inference</title>
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    <name type="personal">
        <namePart type="given">Adam</namePart>
        <namePart type="family">Poliak</namePart>
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        <namePart type="given">Jason</namePart>
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    <name type="personal">
        <namePart type="given">Aparajita</namePart>
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    <name type="personal">
        <namePart type="given">Rachel</namePart>
        <namePart type="family">Rudinger</namePart>
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    <name type="personal">
        <namePart type="given">Benjamin</namePart>
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            <title>Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics</title>
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        <name type="personal">
            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jonathan</namePart>
            <namePart type="family">Berant</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Lenci</namePart>
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    <abstract>We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.</abstract>
    <identifier type="citekey">poliak-etal-2018-hypothesis</identifier>
    <identifier type="doi">10.18653/v1/S18-2023</identifier>
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        <url>https://aclanthology.org/S18-2023/</url>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Hypothesis Only Baselines in Natural Language Inference
%A Poliak, Adam
%A Naradowsky, Jason
%A Haldar, Aparajita
%A Rudinger, Rachel
%A Van Durme, Benjamin
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F poliak-etal-2018-hypothesis
%X We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.
%R 10.18653/v1/S18-2023
%U https://aclanthology.org/S18-2023/
%U https://doi.org/10.18653/v1/S18-2023
%P 180-191

Markdown (Informal)

[Hypothesis Only Baselines in Natural Language Inference](https://aclanthology.org/S18-2023/) (Poliak et al., *SEM 2018)

• Hypothesis Only Baselines in Natural Language Inference (Poliak et al., *SEM 2018)

ACL

• Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018. Hypothesis Only Baselines in Natural Language Inference. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 180–191, New Orleans, Louisiana. Association for Computational Linguistics.