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        <title>Expectations over Unspoken Alternatives Predict Pragmatic Inferences</title>
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        <namePart type="given">Jennifer</namePart>
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    <abstract>Scalar inferences (SI) are a signature example of how humans interpret language based on unspoken alternatives. While empirical studies have demonstrated that human SI rates are highly variable—both within instances of a single scale, and across different scales—there have been few proposals that quantitatively explain both cross- and within-scale variation. Furthermore, while it is generally assumed that SIs arise through reasoning about unspoken alternatives, it remains debated whether humans reason about alternatives as linguistic forms, or at the level of concepts. Here, we test a shared mechanism explaining SI rates within and across scales: context-driven expectations about the unspoken alternatives. Using neural language models to approximate human predictive distributions, we find that SI rates are captured by the expectedness of the strong scalemate as an alternative. Crucially, however, expectedness robustly predicts cross-scale variation only under a meaning-based view of alternatives. Our results suggest that pragmatic inferences arise from context-driven expectations over alternatives, and these expectations operate at the level of concepts.1</abstract>
    <identifier type="citekey">hu-etal-2023-expectations</identifier>
    <identifier type="doi">10.1162/tacl_a_00579</identifier>
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        <date>2023</date>
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%0 Journal Article
%T Expectations over Unspoken Alternatives Predict Pragmatic Inferences
%A Hu, Jennifer
%A Levy, Roger
%A Degen, Judith
%A Schuster, Sebastian
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F hu-etal-2023-expectations
%X Scalar inferences (SI) are a signature example of how humans interpret language based on unspoken alternatives. While empirical studies have demonstrated that human SI rates are highly variable—both within instances of a single scale, and across different scales—there have been few proposals that quantitatively explain both cross- and within-scale variation. Furthermore, while it is generally assumed that SIs arise through reasoning about unspoken alternatives, it remains debated whether humans reason about alternatives as linguistic forms, or at the level of concepts. Here, we test a shared mechanism explaining SI rates within and across scales: context-driven expectations about the unspoken alternatives. Using neural language models to approximate human predictive distributions, we find that SI rates are captured by the expectedness of the strong scalemate as an alternative. Crucially, however, expectedness robustly predicts cross-scale variation only under a meaning-based view of alternatives. Our results suggest that pragmatic inferences arise from context-driven expectations over alternatives, and these expectations operate at the level of concepts.1
%R 10.1162/tacl_a_00579
%U https://aclanthology.org/2023.tacl-1.50
%U https://doi.org/10.1162/tacl_a_00579
%P 885-901

Markdown (Informal)

[Expectations over Unspoken Alternatives Predict Pragmatic Inferences](https://aclanthology.org/2023.tacl-1.50) (Hu et al., TACL 2023)

• Expectations over Unspoken Alternatives Predict Pragmatic Inferences (Hu et al., TACL 2023)

ACL

• Jennifer Hu, Roger Levy, Judith Degen, and Sebastian Schuster. 2023. Expectations over Unspoken Alternatives Predict Pragmatic Inferences. Transactions of the Association for Computational Linguistics, 11:885–901.