Abstract—In this paper, we describe an attempt at constructing a bidirectional English-Vietnamese statistical machine translation system using MOSES, an open-source toolkit for statistical machine translation. The quality of a statistical machine translation system depends on the bilingual sentence alignment data called parallel corpus. However, Vietnamese is an under-resourced language that is largely left out of initial corpus building efforts. Therefore, we concentrate on building Vietnamese corpora which consist over 880,000 English-Vietnamese sentence pairs and over 11,000,000 Vietnamese monolingual sentences to train the statistical translation model and the language model respectively. According to the obtained BLEU scores, the proposed system outperforms the Google translator and the Microsoft Bing translator in both English to Vietnamese and Vietnamese to English translating.

Index Terms—English-Vietnamese bilingual corpus, MOSES, statistical machine translation.

Nguyen Quang Phuoc and Cheol-Young Ock are with the Department of Electrical/Electronic and Computer Engineering, University of Ulsan, Ulsan, Korea.
Yingxiu Quan is with the NAVER LABS, NAVER Corporation, Gyeonggi-do, Korea.

Cite:Nguyen Quang Phuoc, Yingxiu Quan, Cheol-Young Ock, "Building a Bidirectional English-Vietnamese Statistical Machine Translation System by Using MOSES," International Journal of Computer and Electrical Engineering vol. 8, no. 2, pp. 161-168, 2016.