<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="srivastava-etal-2018-zero">
    <titleInfo>
        <title>Zero-shot Learning of Classifiers from Natural Language Quantification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shashank</namePart>
        <namePart type="family">Srivastava</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Igor</namePart>
        <namePart type="family">Labutov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tom</namePart>
        <namePart type="family">Mitchell</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Humans can efficiently learn new concepts using language. We present a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples. We use semantic parsing to map explanations to probabilistic assertions grounded in latent class labels and observed attributes of unlabeled data, and leverage the differential semantics of linguistic quantifiers (e.g., ‘usually’ vs ‘always’) to drive model training. Experiments on three domains show that the learned classifiers outperform previous approaches for learning with limited data, and are comparable with fully supervised classifiers trained from a small number of labeled examples.</abstract>
    <identifier type="citekey">srivastava-etal-2018-zero</identifier>
    <identifier type="doi">10.18653/v1/P18-1029</identifier>
    <location>
        <url>https://aclanthology.org/P18-1029</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>306</start>
            <end>316</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Zero-shot Learning of Classifiers from Natural Language Quantification
%A Srivastava, Shashank
%A Labutov, Igor
%A Mitchell, Tom
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F srivastava-etal-2018-zero
%X Humans can efficiently learn new concepts using language. We present a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples. We use semantic parsing to map explanations to probabilistic assertions grounded in latent class labels and observed attributes of unlabeled data, and leverage the differential semantics of linguistic quantifiers (e.g., ‘usually’ vs ‘always’) to drive model training. Experiments on three domains show that the learned classifiers outperform previous approaches for learning with limited data, and are comparable with fully supervised classifiers trained from a small number of labeled examples.
%R 10.18653/v1/P18-1029
%U https://aclanthology.org/P18-1029
%U https://doi.org/10.18653/v1/P18-1029
%P 306-316

Markdown (Informal)

[Zero-shot Learning of Classifiers from Natural Language Quantification](https://aclanthology.org/P18-1029) (Srivastava et al., ACL 2018)

• Zero-shot Learning of Classifiers from Natural Language Quantification (Srivastava et al., ACL 2018)

ACL

• Shashank Srivastava, Igor Labutov, and Tom Mitchell. 2018. Zero-shot Learning of Classifiers from Natural Language Quantification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 306–316, Melbourne, Australia. Association for Computational Linguistics.