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    <titleInfo>
        <title>Evaluating a Century of Progress on the Cognitive Science of Adjective Ordering</title>
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        <namePart type="given">Richard</namePart>
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    <abstract>The literature on adjective ordering abounds with proposals meant to account for why certain adjectives appear before others in multi-adjective strings (e.g., the small brown box). However, these proposals have been developed and tested primarily in isolation and based on English; few researchers have looked at the combined performance of multiple factors in the determination of adjective order, and few have evaluated predictors across multiple languages. The current work approaches both of these objectives by using technologies and datasets from natural language processing to look at the combined performance of existing proposals across 32 languages. Comparing this performance with both random and idealized baselines, we show that the literature on adjective ordering has made significant meaningful progress across its many decades, but there remains quite a gap yet to be explained.</abstract>
    <identifier type="citekey">dyer-etal-2023-evaluating</identifier>
    <identifier type="doi">10.1162/tacl_a_00596</identifier>
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        <date>2023</date>
        <detail type="volume"><number>11</number></detail>
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            <start>1185</start>
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%0 Journal Article
%T Evaluating a Century of Progress on the Cognitive Science of Adjective Ordering
%A Dyer, William
%A Torres, Charles
%A Scontras, Gregory
%A Futrell, Richard
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F dyer-etal-2023-evaluating
%X The literature on adjective ordering abounds with proposals meant to account for why certain adjectives appear before others in multi-adjective strings (e.g., the small brown box). However, these proposals have been developed and tested primarily in isolation and based on English; few researchers have looked at the combined performance of multiple factors in the determination of adjective order, and few have evaluated predictors across multiple languages. The current work approaches both of these objectives by using technologies and datasets from natural language processing to look at the combined performance of existing proposals across 32 languages. Comparing this performance with both random and idealized baselines, we show that the literature on adjective ordering has made significant meaningful progress across its many decades, but there remains quite a gap yet to be explained.
%R 10.1162/tacl_a_00596
%U https://aclanthology.org/2023.tacl-1.67
%U https://doi.org/10.1162/tacl_a_00596
%P 1185-1200

Markdown (Informal)

[Evaluating a Century of Progress on the Cognitive Science of Adjective Ordering](https://aclanthology.org/2023.tacl-1.67) (Dyer et al., TACL 2023)

• Evaluating a Century of Progress on the Cognitive Science of Adjective Ordering (Dyer et al., TACL 2023)

ACL

• William Dyer, Charles Torres, Gregory Scontras, and Richard Futrell. 2023. Evaluating a Century of Progress on the Cognitive Science of Adjective Ordering. Transactions of the Association for Computational Linguistics, 11:1185–1200.