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Korean-Chinese is a low resource language pair, but Korean and Chinese have alot in common in terms of vocabulary. Sino-Korean words, which can be convertedinto corresponding Chinese characters, account for more than fifty of theentire Korean vocabulary. Motivated by this, we propose a simple linguisticallymotivated solution to improve the performance of the Korean-to-Chinese neuralmachine translation model by using their common vocabulary. We adopt Chinesecharacters as a translation pivot by converting Sino-Korean words in Koreansentences to Chinese characters and then train the machine translation modelwith the converted Korean sentences as source sentences. The experimentalresults on Korean-to-Chinese translation demonstrate that the models with theproposed method improve translation quality up to 1.5 BLEU points in comparisonto the baseline models.

Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue