| Summary: | 碩士 === 健行科技大學 === 資訊工程系碩士班 === 105 === The development of the translation system facilitates communication between different nationalities such as travel, work or meetings. One-to-one translation needs to create large numbers of database, is very large and high management complexity. Most of the technology at this time is to combine voice recognition, machine translation and speech synthesis. The source language voice input, after the voice recognition into text, and then used the machine translation to translation the text to target language text. Finally used speech synthesis output target language voice.
In this study, we combined a neural machine translation and interlingual translation to construct a one-to-many multilingual language translation system to reduce a large amounts of database for ont-to-one translation. Bilingual evaluation understudy(BLEU) algorithm used for evaluating the quality of translation. The 2-gram method proposed to calibrate these quality of translations under a threshold after BLEU. Experiment results showed the Chinese or Japanese interlingual languages after BLEU needed for calibration. The Chinese as interlingual language, the performances of source languages from Japanese and English parts after calibrations increased 67.54% and 61.45%, respectively. The performances of target languages English and Japanese parts after calibrations increased 22.93% and 362.59%, respectively. The interlingual languages used Japanese, the source languages from Chinese and English increased 70.86% and 195.18%, respectively. The performances of the target languages English and Chinese increased 9.81% and 11.67%, respectively.
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