Exploiting parallel corpus for handling out-of-vocabulary words

This paper presents a hybrid model for handling out-of-vocabulary words in Japanese to- English statistical machine translation output by exploiting parallel corpus. As the Japanese writing system makes use of four different script sets (kanji, hiragana, katakana, and romaji), we treat these scripts differently. A machine transliteration model is built to transliterate out-of vocabulary Japanese katakana words into English words. A Japanese dependency structure analyzer is employed to tackle out of-vocabulary kanji and hiragana words. The evaluation results demonstrate that it is an effective approach for addressing out-of vocabulary word problems and decreasing the OOVs rate in the Japanese-to-English machine translation tasks.