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        <title>Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging</title>
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        <namePart type="given">Matthias</namePart>
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        <namePart type="given">Diana</namePart>
        <namePart type="family">Dutka</namePart>
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            <namePart type="given">Preslav</namePart>
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    <abstract>We tackle the important task of part-of-speech tagging using a neural model in the zero-resource scenario, where we have no access to gold-standard POS training data. We compare this scenario with the low-resource scenario, where we have access to a small amount of gold-standard POS training data. Our experiments focus on Ukrainian as a representative of under-resourced languages. Russian is highly related to Ukrainian, so we exploit gold-standard Russian POS tags. We consider four techniques to perform Ukrainian POS tagging: zero-shot tagging and cross-lingual annotation projection (for the zero-resource scenario), and compare these with self-training and multilingual learning (for the low-resource scenario). We find that cross-lingual annotation projection works particularly well in the zero-resource scenario.</abstract>
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    <identifier type="doi">10.18653/v1/W19-1425</identifier>
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%0 Conference Proceedings
%T Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging
%A Huck, Matthias
%A Dutka, Diana
%A Fraser, Alexander
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Malmasi, Shervin
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Ali, Ahmed
%S Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2019
%8 June
%I Association for Computational Linguistics
%C Ann Arbor, Michigan
%F huck-etal-2019-cross
%X We tackle the important task of part-of-speech tagging using a neural model in the zero-resource scenario, where we have no access to gold-standard POS training data. We compare this scenario with the low-resource scenario, where we have access to a small amount of gold-standard POS training data. Our experiments focus on Ukrainian as a representative of under-resourced languages. Russian is highly related to Ukrainian, so we exploit gold-standard Russian POS tags. We consider four techniques to perform Ukrainian POS tagging: zero-shot tagging and cross-lingual annotation projection (for the zero-resource scenario), and compare these with self-training and multilingual learning (for the low-resource scenario). We find that cross-lingual annotation projection works particularly well in the zero-resource scenario.
%R 10.18653/v1/W19-1425
%U https://aclanthology.org/W19-1425
%U https://doi.org/10.18653/v1/W19-1425
%P 223-233

Markdown (Informal)

[Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging](https://aclanthology.org/W19-1425) (Huck et al., VarDial 2019)

• Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging (Huck et al., VarDial 2019)

ACL

• Matthias Huck, Diana Dutka, and Alexander Fraser. 2019. Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 223–233, Ann Arbor, Michigan. Association for Computational Linguistics.