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        <title>A Deep Learning Based Approach to Transliteration</title>
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    <name type="personal">
        <namePart type="given">Soumyadeep</namePart>
        <namePart type="family">Kundu</namePart>
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    <name type="personal">
        <namePart type="given">Sayantan</namePart>
        <namePart type="family">Paul</namePart>
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    <name type="personal">
        <namePart type="given">Santanu</namePart>
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            <title>Proceedings of the Seventh Named Entities Workshop</title>
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            <namePart type="given">Nancy</namePart>
            <namePart type="family">Chen</namePart>
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        <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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        <name type="personal">
            <namePart type="given">Min</namePart>
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        <name type="personal">
            <namePart type="given">Haizhou</namePart>
            <namePart type="family">Li</namePart>
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    <abstract>In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural language processing (NLP) applications. Though a number of statistical models for transliteration have already been proposed in the past few decades, we proposed some neural network based deep learning architectures for the transliteration of named entities. Our transliteration systems adapt two different neural machine translation (NMT) frameworks: recurrent neural network and convolutional sequence to sequence based NMT. It is shown that our method provides quite satisfactory results when it comes to multi lingual machine transliteration. Our submitted runs are an ensemble of different transliteration systems for all the language pairs. In the NEWS 2018 Shared Task on Transliteration, our method achieves top performance for the En–Pe and Pe–En language pairs and comparable results for other cases.</abstract>
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    <identifier type="doi">10.18653/v1/W18-2411</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T A Deep Learning Based Approach to Transliteration
%A Kundu, Soumyadeep
%A Paul, Sayantan
%A Pal, Santanu
%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 kundu-etal-2018-deep
%X In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural language processing (NLP) applications. Though a number of statistical models for transliteration have already been proposed in the past few decades, we proposed some neural network based deep learning architectures for the transliteration of named entities. Our transliteration systems adapt two different neural machine translation (NMT) frameworks: recurrent neural network and convolutional sequence to sequence based NMT. It is shown that our method provides quite satisfactory results when it comes to multi lingual machine transliteration. Our submitted runs are an ensemble of different transliteration systems for all the language pairs. In the NEWS 2018 Shared Task on Transliteration, our method achieves top performance for the En–Pe and Pe–En language pairs and comparable results for other cases.
%R 10.18653/v1/W18-2411
%U https://aclanthology.org/W18-2411
%U https://doi.org/10.18653/v1/W18-2411
%P 79-83

Markdown (Informal)

[A Deep Learning Based Approach to Transliteration](https://aclanthology.org/W18-2411) (Kundu et al., NEWS 2018)

• A Deep Learning Based Approach to Transliteration (Kundu et al., NEWS 2018)

ACL

• Soumyadeep Kundu, Sayantan Paul, and Santanu Pal. 2018. A Deep Learning Based Approach to Transliteration. In Proceedings of the Seventh Named Entities Workshop, pages 79–83, Melbourne, Australia. Association for Computational Linguistics.