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        <title>Building English-to-Serbian Machine Translation System for IMDb Movie Reviews</title>
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            <title>Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing</title>
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            <namePart type="given">Tomaž</namePart>
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            <namePart type="given">Michał</namePart>
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            <namePart type="given">Lidia</namePart>
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    <abstract>This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic in-domain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this low-resource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically.</abstract>
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%0 Conference Proceedings
%T Building English-to-Serbian Machine Translation System for IMDb Movie Reviews
%A Lohar, Pintu
%A Popović, Maja
%A Way, Andy
%Y Erjavec, Tomaž
%Y Marcińczuk, Michał
%Y Nakov, Preslav
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F lohar-etal-2019-building
%X This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic in-domain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this low-resource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically.
%R 10.18653/v1/W19-3715
%U https://aclanthology.org/W19-3715
%U https://doi.org/10.18653/v1/W19-3715
%P 105-113

Markdown (Informal)

[Building English-to-Serbian Machine Translation System for IMDb Movie Reviews](https://aclanthology.org/W19-3715) (Lohar et al., BSNLP 2019)

• Building English-to-Serbian Machine Translation System for IMDb Movie Reviews (Lohar et al., BSNLP 2019)

ACL

• Pintu Lohar, Maja Popović, and Andy Way. 2019. Building English-to-Serbian Machine Translation System for IMDb Movie Reviews. In Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, pages 105–113, Florence, Italy. Association for Computational Linguistics.