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        <title>Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages</title>
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        <namePart type="given">Ngoc</namePart>
        <namePart type="given">Tan</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">Xiangyu</namePart>
            <namePart type="family">Duan</namePart>
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        <name type="personal">
            <namePart type="given">Haizhou</namePart>
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    <abstract>Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. We participated in the NEWS 2018 shared task for the English-Vietnamese transliteration task.</abstract>
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%0 Conference Proceedings
%T Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages
%A Le, Ngoc Tan
%A Sadat, Fatiha
%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 le-sadat-2018-low
%X Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. We participated in the NEWS 2018 shared task for the English-Vietnamese transliteration task.
%R 10.18653/v1/W18-2414
%U https://aclanthology.org/W18-2414
%U https://doi.org/10.18653/v1/W18-2414
%P 95-100

Markdown (Informal)

[Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages](https://aclanthology.org/W18-2414) (Le & Sadat, NEWS 2018)

• Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages (Le & Sadat, NEWS 2018)

ACL

• Ngoc Tan Le and Fatiha Sadat. 2018. Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages. In Proceedings of the Seventh Named Entities Workshop, pages 95–100, Melbourne, Australia. Association for Computational Linguistics.