<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="monroe-etal-2017-colors">
    <titleInfo>
        <title>Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Will</namePart>
        <namePart type="family">Monroe</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Robert</namePart>
        <namePart type="given">X.D.</namePart>
        <namePart type="family">Hawkins</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Noah</namePart>
        <namePart type="given">D</namePart>
        <namePart type="family">Goodman</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="family">Potts</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework. Experiments show that this combined pragmatic model interprets color descriptions more accurately than the classifiers from which it is built, and that much of this improvement results from combining the speaker and listener perspectives. We observe that pragmatic reasoning helps primarily in the hardest cases: when the model must distinguish very similar colors, or when few utterances adequately express the target color. Our findings make use of a newly-collected corpus of human utterances in color reference games, which exhibit a variety of pragmatic behaviors. We also show that the embedded speaker model reproduces many of these pragmatic behaviors.</abstract>
    <identifier type="citekey">monroe-etal-2017-colors</identifier>
    <identifier type="doi">10.1162/tacl_a_00064</identifier>
    <location>
        <url>https://aclanthology.org/Q17-1023</url>
    </location>
    <part>
        <date>2017</date>
        <detail type="volume"><number>5</number></detail>
        <extent unit="page">
            <start>325</start>
            <end>338</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
%A Monroe, Will
%A Hawkins, Robert X.D.
%A Goodman, Noah D.
%A Potts, Christopher
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F monroe-etal-2017-colors
%X We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework. Experiments show that this combined pragmatic model interprets color descriptions more accurately than the classifiers from which it is built, and that much of this improvement results from combining the speaker and listener perspectives. We observe that pragmatic reasoning helps primarily in the hardest cases: when the model must distinguish very similar colors, or when few utterances adequately express the target color. Our findings make use of a newly-collected corpus of human utterances in color reference games, which exhibit a variety of pragmatic behaviors. We also show that the embedded speaker model reproduces many of these pragmatic behaviors.
%R 10.1162/tacl_a_00064
%U https://aclanthology.org/Q17-1023
%U https://doi.org/10.1162/tacl_a_00064
%P 325-338

Markdown (Informal)

[Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://aclanthology.org/Q17-1023) (Monroe et al., TACL 2017)

• Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding (Monroe et al., TACL 2017)

ACL

• Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, and Christopher Potts. 2017. Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding. Transactions of the Association for Computational Linguistics, 5:325–338.