<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sharoff-2017-toward">
    <titleInfo>
        <title>Toward Pan-Slavic NLP: Some Experiments with Language Adaptation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Serge</namePart>
        <namePart type="family">Sharoff</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tomaž</namePart>
            <namePart type="family">Erjavec</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jakub</namePart>
            <namePart type="family">Piskorski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lidia</namePart>
            <namePart type="family">Pivovarova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Šnajder</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Josef</namePart>
            <namePart type="family">Steinberger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roman</namePart>
            <namePart type="family">Yangarber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Valencia, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>There is great variation in the amount of NLP resources available for Slavonic languages. For example, the Universal Dependency treebank (Nivre et al., 2016) has about 2 MW of training resources for Czech, more than 1 MW for Russian, while only 950 words for Ukrainian and nothing for Belorussian, Bosnian or Macedonian. Similarly, the Autodesk Machine Translation dataset only covers three Slavonic languages (Czech, Polish and Russian). In this talk I will discuss a general approach, which can be called Language Adaptation, similarly to Domain Adaptation. In this approach, a model for a particular language processing task is built by lexical transfer of cognate words and by learning a new feature representation for a lesser-resourced (recipient) language starting from a better-resourced (donor) language. More specifically, I will demonstrate how language adaptation works in such training scenarios as Translation Quality Estimation, Part-of-Speech tagging and Named Entity Recognition.</abstract>
    <identifier type="citekey">sharoff-2017-toward</identifier>
    <identifier type="doi">10.18653/v1/W17-1401</identifier>
    <location>
        <url>https://aclanthology.org/W17-1401</url>
    </location>
    <part>
        <date>2017-04</date>
        <extent unit="page">
            <start>1</start>
            <end>2</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Toward Pan-Slavic NLP: Some Experiments with Language Adaptation
%A Sharoff, Serge
%Y Erjavec, Tomaž
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F sharoff-2017-toward
%X There is great variation in the amount of NLP resources available for Slavonic languages. For example, the Universal Dependency treebank (Nivre et al., 2016) has about 2 MW of training resources for Czech, more than 1 MW for Russian, while only 950 words for Ukrainian and nothing for Belorussian, Bosnian or Macedonian. Similarly, the Autodesk Machine Translation dataset only covers three Slavonic languages (Czech, Polish and Russian). In this talk I will discuss a general approach, which can be called Language Adaptation, similarly to Domain Adaptation. In this approach, a model for a particular language processing task is built by lexical transfer of cognate words and by learning a new feature representation for a lesser-resourced (recipient) language starting from a better-resourced (donor) language. More specifically, I will demonstrate how language adaptation works in such training scenarios as Translation Quality Estimation, Part-of-Speech tagging and Named Entity Recognition.
%R 10.18653/v1/W17-1401
%U https://aclanthology.org/W17-1401
%U https://doi.org/10.18653/v1/W17-1401
%P 1-2

Markdown (Informal)

[Toward Pan-Slavic NLP: Some Experiments with Language Adaptation](https://aclanthology.org/W17-1401) (Sharoff, BSNLP 2017)

• Toward Pan-Slavic NLP: Some Experiments with Language Adaptation (Sharoff, BSNLP 2017)

ACL

• Serge Sharoff. 2017. Toward Pan-Slavic NLP: Some Experiments with Language Adaptation. In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing, pages 1–2, Valencia, Spain. Association for Computational Linguistics.