HERIT: Enabling Global Access to Hanja-to-English Historical Translation by Mitigating Temporal Bias

JeongDayeon, Kyeongpil Kang

HERIT: Enabling Global Access to Hanja-to-English Historical Translation by Mitigating Temporal Bias: 1 upvotes on Hugging Face Daily Papers, #37 of 38 papers on 2026-11-05. Day-by-day upvote history.

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It had 0 upvotes by its first day, 1 upvotes by the next day, 1 upvotes by day 3, 1 upvotes by day 7, and has 1 now. It ranked #199 of 207 papers in its week and #324 of 340 in its month. Its code repository has 1 GitHub stars.

Historical archives are invaluable for multidisciplinary research but remain inaccessible globally due to severe language barriers. While manual translation is prohibitively expensive and time-consuming, simply applying existing machine translation models is inadequate due to unique linguistic nuances and the temporal bias inherent in scarce human-labeled corpora. To address these challenges, we propose HERIT, a systematic domain-adaptation pipeline that leverages retrieval-augmented generation (RAG) to generate high-quality pseudo-labeled data from abundant Hanja-Korean corpora. This approach substantially expands the training dataset, mitigating both data scarcity and temporal overfitting across different historical periods. Extensive automated and human evaluations demonstrate that HERIT consistently outperforms baselines. Finally, we translate previously untranslated portions of the archives using our model, thereby enabling global access to these resources for researchers.

Paper page on Hugging Face · arXiv

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