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    <titleInfo>
        <title>Neural Machine Translation Techniques for Named Entity Transliteration</title>
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    <name type="personal">
        <namePart type="given">Roman</namePart>
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        <titleInfo>
            <title>Proceedings of the Seventh Named Entities Workshop</title>
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        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Rafael</namePart>
            <namePart type="given">E</namePart>
            <namePart type="family">Banchs</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Xiangyu</namePart>
            <namePart type="family">Duan</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
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        <name type="personal">
            <namePart type="given">Haizhou</namePart>
            <namePart type="family">Li</namePart>
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        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
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                <placeTerm type="text">Melbourne, Australia</placeTerm>
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    <abstract>Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models. To build a strong transliteration system, we apply well-established techniques from NMT, such as dropout regularization, model ensembling, rescoring with right-to-left models, and back-translation. Our submission to the NEWS 2018 Shared Task on Named Entity Transliteration ranked first in several tracks.</abstract>
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    <identifier type="doi">10.18653/v1/W18-2413</identifier>
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        <date>2018-07</date>
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            <start>89</start>
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%0 Conference Proceedings
%T Neural Machine Translation Techniques for Named Entity Transliteration
%A Grundkiewicz, Roman
%A Heafield, Kenneth
%Y Chen, Nancy
%Y Banchs, Rafael E.
%Y Duan, Xiangyu
%Y Zhang, Min
%Y Li, Haizhou
%S Proceedings of the Seventh Named Entities Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F grundkiewicz-heafield-2018-neural
%X Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models. To build a strong transliteration system, we apply well-established techniques from NMT, such as dropout regularization, model ensembling, rescoring with right-to-left models, and back-translation. Our submission to the NEWS 2018 Shared Task on Named Entity Transliteration ranked first in several tracks.
%R 10.18653/v1/W18-2413
%U https://aclanthology.org/W18-2413
%U https://doi.org/10.18653/v1/W18-2413
%P 89-94

Markdown (Informal)

[Neural Machine Translation Techniques for Named Entity Transliteration](https://aclanthology.org/W18-2413) (Grundkiewicz & Heafield, NEWS 2018)

• Neural Machine Translation Techniques for Named Entity Transliteration (Grundkiewicz & Heafield, NEWS 2018)

ACL

• Roman Grundkiewicz and Kenneth Heafield. 2018. Neural Machine Translation Techniques for Named Entity Transliteration. In Proceedings of the Seventh Named Entities Workshop, pages 89–94, Melbourne, Australia. Association for Computational Linguistics.