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        <title>English-Indonesian Neural Machine Translation for Spoken Language Domains</title>
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        <namePart type="given">Meisyarah</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
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    <abstract>In this work, we conduct a study on Neural Machine Translation (NMT) for English-Indonesian (EN-ID) and Indonesian-English (ID-EN). We focus on spoken language domains, namely colloquial and speech languages. We build NMT systems using the Transformer model for both translation directions and implement domain adaptation, in which we train our pre-trained NMT systems on speech language (in-domain) data. Moreover, we conduct an evaluation on how the domain-adaptation method in our EN-ID system can result in more formal translation outputs.</abstract>
    <identifier type="citekey">dwiastuti-2019-english</identifier>
    <identifier type="doi">10.18653/v1/P19-2043</identifier>
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%0 Conference Proceedings
%T English-Indonesian Neural Machine Translation for Spoken Language Domains
%A Dwiastuti, Meisyarah
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F dwiastuti-2019-english
%X In this work, we conduct a study on Neural Machine Translation (NMT) for English-Indonesian (EN-ID) and Indonesian-English (ID-EN). We focus on spoken language domains, namely colloquial and speech languages. We build NMT systems using the Transformer model for both translation directions and implement domain adaptation, in which we train our pre-trained NMT systems on speech language (in-domain) data. Moreover, we conduct an evaluation on how the domain-adaptation method in our EN-ID system can result in more formal translation outputs.
%R 10.18653/v1/P19-2043
%U https://aclanthology.org/P19-2043
%U https://doi.org/10.18653/v1/P19-2043
%P 309-314

Markdown (Informal)

[English-Indonesian Neural Machine Translation for Spoken Language Domains](https://aclanthology.org/P19-2043) (Dwiastuti, ACL 2019)

• English-Indonesian Neural Machine Translation for Spoken Language Domains (Dwiastuti, ACL 2019)

ACL

• Meisyarah Dwiastuti. 2019. English-Indonesian Neural Machine Translation for Spoken Language Domains. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 309–314, Florence, Italy. Association for Computational Linguistics.