Translating Hanja Historical Documents to Contemporary K orean and E nglish (2022)

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only one king’s records in English so far.Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English.Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English.We compare our method against two baselines:a recent model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 documentand 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.

## introduction historical documents written in an archaic language should be translated into a modern language. most of the korean historical documents are written in hanja, the main written language in korea machine translation can accelerate the translation process. the challenge is the limited availability of parallel corpora between hanja to contemporary korean as well as english. only one annal of the 24 kings of the joseon dynasty was newly translated into korean and english. this is not a sufficient amount to train a full machine translation model. to address this low-resource problem, we adopt a multilingual translation approach that jointly learns to translate between hanja, outdated original korean, contemporary korean and english, expecting positive transfer of knowledge among these languages. we present a multilingual neural machine translation model that translates hanja historical documents to contemporary korean, to which we refer as h2ke. by exploiting extra resources, h2ke performs significantly better translation of hanja into contemporary korean than other approaches that rely solely on the parallel corpus from the newly translated korean and hanja. we measure the perplexity with a large-scale language model trained on contemporary korean, called kogpt @xcite , to show that translations from our model are more similar to contemporary korean than the old korean translations from the original translation effort. these results are further confirmed by human evaluation, where both experts and non-experts prefer our model's translation over the original translation in old korean. using h2ke, we translated the remaining ajd to contemporary korean as well as english and are releasing it publicly at https://juheeuu.github. io/h2ke-demo . our main contributions include: ## human evaluation we conduct human evaluation of korean translations to confirm that h2ke's translations are both more understandable and accurate than the groundtruth oko. we use the direct assessment (da) @xcite @xcite as the primary method for evaluating translation systems, where the crowd-sourced bilingual human assessors are asked to rate a translation given the source sentences by how adequately it expresses the meaning of the sentences in an analog scale @xcite . we cannot however adopt the crowd-sourced da approach as is because only a few historians can evaluate the meaning of translations by interpreting hanja. we thus work together with itkc and ask their experts to evaluate our generated translations according to their internal evaluation criteria. this is the same procedure taken to ensure the quality of human translations at itkc. additionally, we conduct another evaluation to confirm whether the new korean translation improves the understanding of historical documents for non-expert korean speakers. ## conclusion we present h2ke, a neural machine translation system for the ajd that translates from hanja to contemporary korean and english. h2ke is built on top of mnmt systems to overcome the low-resource training data problem. h2ke shows a significantly higher bleu score than the baseline and a current commercial translation system. based on the perplexity evaluation with kogpt, the translation samples from h2ke are closer to the contemporary korean corpus than the ground truth original korean translations and the baseline. the human evaluation results show that the translation samples from h2ke are more accurate and understandable than the ground truth original korean. finally, we translate the entire ajd to contemporary korean and english with h2ke and publicly release the translations. in this work, we provide strong evidence that existing algorithms for machine translation and natural language processing generalize to a scenario where data span several centuries of an archaic language. it is highly technical in that it leads to a deeper understanding of existing algorithms and significantly extends the scope of the previous studies.