A Fast Decoder Using Less Memory
Hien Vo Minh, Điền Đinh, Nhung Nguyen Thi Hong · 2012
Statistical Machine Translation (SMT) uses large amount of text corpus and complex calculation operation for translation process, which makes this method require more system resources for fast translation. In this paper, we introduce an approach of decoding in SMT using less memory but translating faster, which is more suitable for mobile applications and embedded systems. In our approach, the SMT models are stored in tree structures in order to speed up the loading process and the decoding algorithm is optimized to reduce operations. We apply our approach to English-Vietnamese and Vietnamese-English SMT systems. When translating 20,000 English sentences, which are 7.45 word lengths in average, we achieve 37.8 BLEU score, the average speed is 0.052 s. In case of Vietnamese-English system, we translate 20,000 Vietnamese sentences, which are 8.42 word lengths in average, the BLEU score is 34.63 with an average speed of 0.091 s.