Jeonghui Kim  Jaemu Heo  Joowhan Kim  Heeyoul Choi

10.5626/JOK.2022.49.11.1017

neural machine translation Korean words jamo honorifics

Recently, deep learning has improved the performance of machine translation, but in most cases, it does not reflect the characteristics of the languages. In particular, Korean has unique linguistic word and expression features, which might cause mistranslation. For example, in Google Translate from Korean to English, mistranslations occur when a noun in Korean ends with the postposition (josa) in the form of a single consonant. Also, in the English-Korean translations, the honorifics and casual expressions are mixed in the translated results. This is because the alphabetic characteristics and honorifics of the Korean language are not reflected. In this paper, to address these problems, we propose to train a model with sub-words composed of units of letters (jamo) and unifying honorific and casual expressions in the corpus. The experimental results confirmed that the proposed method resolved the problems mentioned above, and had a similar or slightly higher BLEU score compared to the existing method and the corpus.