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        <title>On Machine Translation of User Reviews</title>
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        <namePart type="given">Maja</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>This work investigates neural machine translation (NMT) systems for translating English user reviews into Croatian and Serbian, two similar morphologically complex languages. Two types of reviews are used for testing the systems: IMDb movie reviews and Amazon product reviews. Two types of training data are explored: large out-of-domain bilingual parallel corpora, as well as small synthetic in-domain parallel corpus obtained by machine translation of monolingual English Amazon reviews into the target languages. Both automatic scores and human evaluation show that using the synthetic in-domain corpus together with a selected sub-set of out-of-domain data is the best option. Separated results on IMDb and Amazon reviews indicate that MT systems perform differently on different review types so that user reviews generally should not be considered as a homogeneous genre. Nevertheless, more detailed research on larger amount of different reviews covering different domains/topics is needed to fully understand these differences.</abstract>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T On Machine Translation of User Reviews
%A Popović, Maja
%A Poncelas, Alberto
%A Brkic, Marija
%A Way, Andy
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F popovic-etal-2021-machine
%X This work investigates neural machine translation (NMT) systems for translating English user reviews into Croatian and Serbian, two similar morphologically complex languages. Two types of reviews are used for testing the systems: IMDb movie reviews and Amazon product reviews. Two types of training data are explored: large out-of-domain bilingual parallel corpora, as well as small synthetic in-domain parallel corpus obtained by machine translation of monolingual English Amazon reviews into the target languages. Both automatic scores and human evaluation show that using the synthetic in-domain corpus together with a selected sub-set of out-of-domain data is the best option. Separated results on IMDb and Amazon reviews indicate that MT systems perform differently on different review types so that user reviews generally should not be considered as a homogeneous genre. Nevertheless, more detailed research on larger amount of different reviews covering different domains/topics is needed to fully understand these differences.
%U https://aclanthology.org/2021.ranlp-1.124
%P 1109-1118

Markdown (Informal)

[On Machine Translation of User Reviews](https://aclanthology.org/2021.ranlp-1.124) (Popović et al., RANLP 2021)

• On Machine Translation of User Reviews (Popović et al., RANLP 2021)

ACL

• Maja Popović, Alberto Poncelas, Marija Brkic, and Andy Way. 2021. On Machine Translation of User Reviews. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1109–1118, Held Online. INCOMA Ltd..