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        <title>Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition</title>
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        <namePart type="given">Hwichan</namePart>
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            <namePart type="family">Rijhwani</namePart>
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        <name type="personal">
            <namePart type="given">Jiangming</namePart>
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    <abstract>The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translation accuracy cannot be achieved. To address this problem, we propose a zero-shot approach using South Korean data, which are remarkably similar to North Korean data. We train a neural machine translation model after tokenizing a South Korean text at the character level and decomposing characters into phonemes. We demonstrate that our method can effectively learn North Korean to English translation and improve the BLEU scores by +1.01 points in comparison with the baseline.</abstract>
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%0 Conference Proceedings
%T Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition
%A Kim, Hwichan
%A Hirasawa, Tosho
%A Komachi, Mamoru
%Y Rijhwani, Shruti
%Y Liu, Jiangming
%Y Wang, Yizhong
%Y Dror, Rotem
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F kim-etal-2020-zero
%X The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translation accuracy cannot be achieved. To address this problem, we propose a zero-shot approach using South Korean data, which are remarkably similar to North Korean data. We train a neural machine translation model after tokenizing a South Korean text at the character level and decomposing characters into phonemes. We demonstrate that our method can effectively learn North Korean to English translation and improve the BLEU scores by +1.01 points in comparison with the baseline.
%R 10.18653/v1/2020.acl-srw.11
%U https://aclanthology.org/2020.acl-srw.11
%U https://doi.org/10.18653/v1/2020.acl-srw.11
%P 72-78

Markdown (Informal)

[Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition](https://aclanthology.org/2020.acl-srw.11) (Kim et al., ACL 2020)

• Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition (Kim et al., ACL 2020)

ACL

• Hwichan Kim, Tosho Hirasawa, and Mamoru Komachi. 2020. Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 72–78, Online. Association for Computational Linguistics.