<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="briggs-harner-2019-generating">
    <titleInfo>
        <title>Generating Quantified Referring Expressions with Perceptual Cost Pruning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Gordon</namePart>
        <namePart type="family">Briggs</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hillary</namePart>
        <namePart type="family">Harner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-oct–nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kees</namePart>
            <namePart type="family">van Deemter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chenghua</namePart>
            <namePart type="family">Lin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hiroya</namePart>
            <namePart type="family">Takamura</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Tokyo, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We model the production of quantified referring expressions (QREs) that identify collections of visual items. To address this task, we propose a method of perceptual cost pruning, which consists of two steps: (1) determine what subset of quantity information can be perceived given a time limit t, and (2) apply a preference order based REG algorithm (e.g., the Incremental Algorithm) to this reduced set of information. We demonstrate that this method successfully improves the human-likeness of the IA in the QRE generation task and successfully models human-generated language in most cases.</abstract>
    <identifier type="citekey">briggs-harner-2019-generating</identifier>
    <identifier type="doi">10.18653/v1/W19-8602</identifier>
    <location>
        <url>https://aclanthology.org/W19-8602</url>
    </location>
    <part>
        <date>2019-oct–nov</date>
        <extent unit="page">
            <start>11</start>
            <end>18</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generating Quantified Referring Expressions with Perceptual Cost Pruning
%A Briggs, Gordon
%A Harner, Hillary
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F briggs-harner-2019-generating
%X We model the production of quantified referring expressions (QREs) that identify collections of visual items. To address this task, we propose a method of perceptual cost pruning, which consists of two steps: (1) determine what subset of quantity information can be perceived given a time limit t, and (2) apply a preference order based REG algorithm (e.g., the Incremental Algorithm) to this reduced set of information. We demonstrate that this method successfully improves the human-likeness of the IA in the QRE generation task and successfully models human-generated language in most cases.
%R 10.18653/v1/W19-8602
%U https://aclanthology.org/W19-8602
%U https://doi.org/10.18653/v1/W19-8602
%P 11-18

Markdown (Informal)

[Generating Quantified Referring Expressions with Perceptual Cost Pruning](https://aclanthology.org/W19-8602) (Briggs & Harner, INLG 2019)

• Generating Quantified Referring Expressions with Perceptual Cost Pruning (Briggs & Harner, INLG 2019)

ACL

• Gordon Briggs and Hillary Harner. 2019. Generating Quantified Referring Expressions with Perceptual Cost Pruning. In Proceedings of the 12th International Conference on Natural Language Generation, pages 11–18, Tokyo, Japan. Association for Computational Linguistics.