Korean Morphological Analysis for Korean-Vietnamese Statistical Machine Translation
Quang-Phuoc Nguyen, Joon-Choul Shin, Cheol-Young Ock · 2017
This paper describes the experiments with Korean-to-Vietnamese statistical machine translation (SMT). The fact that Korean is a morphologically complex language that does not have clear optimal word boundaries causes a major problem of translating into or from Korean. To solve this problem, we present a method to conduct a Korean morphological analysis by using a pre-analyzed partial word-phrase dictionary (PWD). Besides, we build a Korean-Vietnamese parallel corpus for training SMT models by collecting text from multilingual magazines. Then, we apply such a morphology analysis to Korean sentences that are included in the collected parallel corpus as a preprocessing step. The experiment results demonstrate a remarkable improvement of Korean-to-Vietnamese translation quality in term of bi-lingual evaluation understudy (BLEU).