<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="feldman-2017-rational">
    <titleInfo>
        <title>Rational Distortions of Learners’ Linguistic Input</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Naomi</namePart>
        <namePart type="family">Feldman</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Roger</namePart>
            <namePart type="family">Levy</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Language acquisition can be modeled as a statistical inference problem: children use sentences and sounds in their input to infer linguistic structure. However, in many cases, children learn from data whose statistical structure is distorted relative to the language they are learning. Such distortions can arise either in the input itself, or as a result of children’s immature strategies for encoding their input. This work examines several cases in which the statistical structure of children’s input differs from the language being learned. Analyses show that these distortions of the input can be accounted for with a statistical learning framework by carefully considering the inference problems that learners solve during language acquisition</abstract>
    <identifier type="citekey">feldman-2017-rational</identifier>
    <identifier type="doi">10.18653/v1/K17-1002</identifier>
    <location>
        <url>https://aclanthology.org/K17-1002</url>
    </location>
    <part>
        <date>2017-08</date>
        <detail type="page"><number>2</number></detail>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Rational Distortions of Learners’ Linguistic Input
%A Feldman, Naomi
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F feldman-2017-rational
%X Language acquisition can be modeled as a statistical inference problem: children use sentences and sounds in their input to infer linguistic structure. However, in many cases, children learn from data whose statistical structure is distorted relative to the language they are learning. Such distortions can arise either in the input itself, or as a result of children’s immature strategies for encoding their input. This work examines several cases in which the statistical structure of children’s input differs from the language being learned. Analyses show that these distortions of the input can be accounted for with a statistical learning framework by carefully considering the inference problems that learners solve during language acquisition
%R 10.18653/v1/K17-1002
%U https://aclanthology.org/K17-1002
%U https://doi.org/10.18653/v1/K17-1002
%P 2

Markdown (Informal)

[Rational Distortions of Learners’ Linguistic Input](https://aclanthology.org/K17-1002) (Feldman, CoNLL 2017)

• Rational Distortions of Learners’ Linguistic Input (Feldman, CoNLL 2017)

ACL

• Naomi Feldman. 2017. Rational Distortions of Learners’ Linguistic Input. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), page 2, Vancouver, Canada. Association for Computational Linguistics.