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    <titleInfo>
        <title>Leveraging Orthographic Similarity for Multilingual Neural Transliteration</title>
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        <namePart type="given">Anoop</namePart>
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    <name type="personal">
        <namePart type="given">Mitesh</namePart>
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    <name type="personal">
        <namePart type="given">Gurneet</namePart>
        <namePart type="family">Singh</namePart>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
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    <abstract>We address the task of joint training of transliteration models for multiple language pairs (multilingual transliteration). This is an instance of multitask learning, where individual tasks (language pairs) benefit from sharing knowledge with related tasks. We focus on transliteration involving related tasks i.e., languages sharing writing systems and phonetic properties (orthographically similar languages). We propose a modified neural encoder-decoder model that maximizes parameter sharing across language pairs in order to effectively leverage orthographic similarity. We show that multilingual transliteration significantly outperforms bilingual transliteration in different scenarios (average increase of 58% across a variety of languages we experimented with). We also show that multilingual transliteration models can generalize well to languages/language pairs not encountered during training and hence perform well on the zeroshot transliteration task. We show that further improvements can be achieved by using phonetic feature input.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00022</identifier>
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        <date>2018</date>
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%0 Journal Article
%T Leveraging Orthographic Similarity for Multilingual Neural Transliteration
%A Kunchukuttan, Anoop
%A Khapra, Mitesh
%A Singh, Gurneet
%A Bhattacharyya, Pushpak
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F kunchukuttan-etal-2018-leveraging
%X We address the task of joint training of transliteration models for multiple language pairs (multilingual transliteration). This is an instance of multitask learning, where individual tasks (language pairs) benefit from sharing knowledge with related tasks. We focus on transliteration involving related tasks i.e., languages sharing writing systems and phonetic properties (orthographically similar languages). We propose a modified neural encoder-decoder model that maximizes parameter sharing across language pairs in order to effectively leverage orthographic similarity. We show that multilingual transliteration significantly outperforms bilingual transliteration in different scenarios (average increase of 58% across a variety of languages we experimented with). We also show that multilingual transliteration models can generalize well to languages/language pairs not encountered during training and hence perform well on the zeroshot transliteration task. We show that further improvements can be achieved by using phonetic feature input.
%R 10.1162/tacl_a_00022
%U https://aclanthology.org/Q18-1022
%U https://doi.org/10.1162/tacl_a_00022
%P 303-316

Markdown (Informal)

[Leveraging Orthographic Similarity for Multilingual Neural Transliteration](https://aclanthology.org/Q18-1022) (Kunchukuttan et al., TACL 2018)

• Leveraging Orthographic Similarity for Multilingual Neural Transliteration (Kunchukuttan et al., TACL 2018)

ACL

• Anoop Kunchukuttan, Mitesh Khapra, Gurneet Singh, and Pushpak Bhattacharyya. 2018. Leveraging Orthographic Similarity for Multilingual Neural Transliteration. Transactions of the Association for Computational Linguistics, 6:303–316.