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Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue
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Korean-Chinese is a low resource language pair, but Korean and Chinese have a lot in common in terms of vocabulary. Sino-Korean words, which can be converted into corresponding Chinese characters, account for more than fifty of the entire Korean vocabulary. Motivated by this, we propose a simple linguistically motivated solution to improve the performance of the Korean-to-Chinese neural machine translation model by using their common vocabulary. We adopt Chinese characters as a translation pivot by converting Sino-Korean words in Korean sentences to Chinese characters and then train the machine translation model with the converted Korean sentences as source sentences. The experimental results on Korean-to-Chinese translation demonstrate that the models with the proposed method improve translation quality up to 1.5 BLEU points in comparison to the baseline models.
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Cited by 1 Pith paper
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HanjaBridge, a continual pre-training method that appends all candidate Hanja forms for Korean homophones, improves KoBALT scores by 21 percent relative while keeping English performance mostly intact.
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