@ARTICLE{Namdar, author = {Namdar, Saman and Faili, Hesham and Khadivi, Shahram and }, title = { Statistical Machine Translation (SMT) for Highly-Inflectional Scarce-Resource Language }, volume = {5}, number = {1}, abstract ={Statistical Machine Translation (SMT) is a machine translation paradigm, in which translations are generated on the base of statistical models. In this system, parameters are derived from an analysis of a parallel corpus, and SMT quality depends on the ability of learning word translations. Enriching the SMT by a suitable morphology analyser decreases out of vocabulary words and dictionary size dramatically. This could be more considerable when it deals with a highly-inflectional, low-resource, language like Persian. Defining a suitable granularity for word segment may improve the alignment quality in the parallel corpus. In this paper different schemes and word’s combinations segments in a SMT’s experiment from Persian to English language are prospected and the best one-to-one alignment, which is called En-like scheme, is proposed. By using the mentioned scheme the translation’s quality from Persian to English is improved about 3 points with respect to BLEU measure over the phrase-based SMT. }, URL = {http://ijict.itrc.ac.ir/article-1-165-en.html}, eprint = {http://ijict.itrc.ac.ir/article-1-165-en.pdf}, journal = {International Journal of Information and Communication Technology Research}, doi = {}, year = {2013} }