An Improved Statistical Machine Translation Method for United Chinese-Japanese Word Segmentation

As Chinese and Japanese word segmentation is processed with different tagging system and semantic performance, the granularity of word segmentation results should be readjusted to improve the performance of Statistical Machine Translation (SMT). This paper proposes an approach to adjust the word segmentation granularity for improving the performance of SMT, which combines Hanzi-Kanji comparison table and Japanese-Chinese dictionary. Experimental results express that the proposed method could adjust the granularity between Chinese and Japanese effectively and improve the performance of SMT.