  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.

Computation and Language