Translating Hanja Historical Documents to Contemporary Korean and English

• URL: http://arxiv.org/abs/2205.10019v5
• Date: Fri, 29 Dec 2023 12:18:45 GMT
• Title: Translating Hanja Historical Documents to Contemporary Korean and English
• Authors: Juhee Son, Jiho Jin, Haneul Yoo, JinYeong Bak, Kyunghyun Cho, Alice Oh
• Abstract summary: Annals of Joseon Dynasty contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, Hanja', and were translated into Korean from 1968 to 1993. Since then, the records of only one king have been completed in a decade. We propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English.
• Score: 52.625998002213585
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012. Since then, the records of only one king have been completed in a decade. In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far. Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English. Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English. We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora. The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations. We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.

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