<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tomoschuk-lovelett-2018-memory">
    <titleInfo>
        <title>A Memory-Sensitive Classification Model of Errors in Early Second Language Learning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Brendan</namePart>
        <namePart type="family">Tomoschuk</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jarrett</namePart>
        <namePart type="family">Lovelett</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Leacock</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">New Orleans, Louisiana</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we explore a variety of linguistic and cognitive features to better understand second language acquisition in early users of the language learning app Duolingo. With these features, we trained a random forest classifier to predict errors in early learners of French, Spanish, and English. Of particular note was our finding that mean and variance in error for each user and token can be a memory efficient replacement for their respective dummy-encoded categorical variables. At test, these models improved over the baseline model with AUROC values of 0.803 for English, 0.823 for French, and 0.829 for Spanish.</abstract>
    <identifier type="citekey">tomoschuk-lovelett-2018-memory</identifier>
    <identifier type="doi">10.18653/v1/W18-0527</identifier>
    <location>
        <url>https://aclanthology.org/W18-0527</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>231</start>
            <end>239</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Memory-Sensitive Classification Model of Errors in Early Second Language Learning
%A Tomoschuk, Brendan
%A Lovelett, Jarrett
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F tomoschuk-lovelett-2018-memory
%X In this paper, we explore a variety of linguistic and cognitive features to better understand second language acquisition in early users of the language learning app Duolingo. With these features, we trained a random forest classifier to predict errors in early learners of French, Spanish, and English. Of particular note was our finding that mean and variance in error for each user and token can be a memory efficient replacement for their respective dummy-encoded categorical variables. At test, these models improved over the baseline model with AUROC values of 0.803 for English, 0.823 for French, and 0.829 for Spanish.
%R 10.18653/v1/W18-0527
%U https://aclanthology.org/W18-0527
%U https://doi.org/10.18653/v1/W18-0527
%P 231-239

Markdown (Informal)

[A Memory-Sensitive Classification Model of Errors in Early Second Language Learning](https://aclanthology.org/W18-0527) (Tomoschuk & Lovelett, BEA 2018)

• A Memory-Sensitive Classification Model of Errors in Early Second Language Learning (Tomoschuk & Lovelett, BEA 2018)

ACL

• Brendan Tomoschuk and Jarrett Lovelett. 2018. A Memory-Sensitive Classification Model of Errors in Early Second Language Learning. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 231–239, New Orleans, Louisiana. Association for Computational Linguistics.