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        <title>Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish</title>
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    <name type="personal">
        <namePart type="given">Benyamin</namePart>
        <namePart type="family">Ahmadnia</namePart>
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        <namePart type="family">Dorr</namePart>
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        <dateIssued>2019-09</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality. We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario. Experimental results demonstrate that this competitive approach outperforms the baselines.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_003</identifier>
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        <url>https://aclanthology.org/R19-1003/</url>
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%0 Conference Proceedings
%T Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish
%A Ahmadnia, Benyamin
%A Dorr, Bonnie
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F ahmadnia-dorr-2019-bilingual
%X The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality. We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario. Experimental results demonstrate that this competitive approach outperforms the baselines.
%R 10.26615/978-954-452-056-4_003
%U https://aclanthology.org/R19-1003/
%U https://doi.org/10.26615/978-954-452-056-4_003
%P 18-24

Markdown (Informal)

[Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish](https://aclanthology.org/R19-1003/) (Ahmadnia & Dorr, RANLP 2019)

• Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish (Ahmadnia & Dorr, RANLP 2019)

ACL

• Benyamin Ahmadnia and Bonnie Dorr. 2019. Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 18–24, Varna, Bulgaria. INCOMA Ltd..