@inproceedings{kim-etal-2020-zero,
title = "Zero-shot {N}orth {K}orean to {E}nglish Neural Machine Translation by Character Tokenization and Phoneme Decomposition",
author = "Kim, Hwichan and
Hirasawa, Tosho and
Komachi, Mamoru",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-srw.11",
doi = "10.18653/v1/2020.acl-srw.11",
pages = "72--78",
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.",
}
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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
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 jul
%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)
ACL