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        <title>Jamo-Level Subword Tokenization in Low-Resource Korean Machine Translation</title>
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    <abstract>Subword tokenization, where text is represented in an intermediate form between full words and characters, is ubiquitous in modern NLP due to its ability to represent any input sentence with a small vocabulary. However for Korean, where there are 11,172 base characters (*syllables*) in its alphabet, it is difficult to have a vocabulary large enough to succinctly encode text while fitting within parameter-budget constraints. This motivates us to explore an alternative representation for Korean which relies on the decompositional nature of Korean syllables: a syllable can be uniquely decomposed into a sequence of two or three subcharacters (*jamo*), of which there are only 68.Using jamo as the basis for subword tokenization (e.g., byte-pair encoding) leads to shorter tokenized sequences with fewer vocabulary parameters, exposes the model to sub-syllable-level morphological information, and increases the amount of augmentation gained from subword regularization. We evaluate jamo-level subword tokenization on several Korean translation tasks and find that jamo-level subword models consistently outperform syllable- and byte-level models in low-resource and restricted-vocabulary settings.</abstract>
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%0 Conference Proceedings
%T Jamo-Level Subword Tokenization in Low-Resource Korean Machine Translation
%A Lee, Junyoung
%A Cognetta, Marco
%A Moon, Sangwhan
%A Okazaki, Naoaki
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, U.S.A.
%@ 979-8-89176-230-5
%F lee-etal-2025-jamo
%X Subword tokenization, where text is represented in an intermediate form between full words and characters, is ubiquitous in modern NLP due to its ability to represent any input sentence with a small vocabulary. However for Korean, where there are 11,172 base characters (*syllables*) in its alphabet, it is difficult to have a vocabulary large enough to succinctly encode text while fitting within parameter-budget constraints. This motivates us to explore an alternative representation for Korean which relies on the decompositional nature of Korean syllables: a syllable can be uniquely decomposed into a sequence of two or three subcharacters (*jamo*), of which there are only 68.Using jamo as the basis for subword tokenization (e.g., byte-pair encoding) leads to shorter tokenized sequences with fewer vocabulary parameters, exposes the model to sub-syllable-level morphological information, and increases the amount of augmentation gained from subword regularization. We evaluate jamo-level subword tokenization on several Korean translation tasks and find that jamo-level subword models consistently outperform syllable- and byte-level models in low-resource and restricted-vocabulary settings.
%R 10.18653/v1/2025.loresmt-1.8
%U https://aclanthology.org/2025.loresmt-1.8/
%U https://doi.org/10.18653/v1/2025.loresmt-1.8
%P 66-80

Markdown (Informal)

[Jamo-Level Subword Tokenization in Low-Resource Korean Machine Translation](https://aclanthology.org/2025.loresmt-1.8/) (Lee et al., LoResMT 2025)

• Jamo-Level Subword Tokenization in Low-Resource Korean Machine Translation (Lee et al., LoResMT 2025)

ACL

• Junyoung Lee, Marco Cognetta, Sangwhan Moon, and Naoaki Okazaki. 2025. Jamo-Level Subword Tokenization in Low-Resource Korean Machine Translation. In Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025), pages 66–80, Albuquerque, New Mexico, U.S.A.. Association for Computational Linguistics.