M. Meshesha, and Y. Solomon. International Journal of Computational Linguistics (IJCL)9
26-31 (April 2018)
Statistical machine translation (SMT) is an approach that mainly uses parallel corpus for translation and its performance is dependent on effectiveness of alignment of source and target languages. This study explores the effect of word, phrase and sentence levels of alignment on English-Afaan Oromo statistical machine translation. We used GIZA++, Anymalignment and hunalign for word level, phrase level and sentence level alignment, respectively. Experimental result shows that 27% BLUE score is recorded at phrase level alignment with maximum phrase length of 16. The Syntactic structure sensitivity of the alignment tool and the challenge of word correspondence variation in the two languages needs further investigation.