Statistical Machine Translation of English – Manipuri using Morpho-syntactic and Semantic Information

English-Manipuri language pair is one of the rarely investigated with restricted bilingual resources. The development of a factored Statistical Machine Translation (SMT) system between English as source and Manipuri, a morphologically rich language as target is reported. The role of the suffixes and dependency relations on the source side and case markers on the target side are identified as important translation factors. The morphology and dependency relations play important roles to improve the translation quality. A parallel corpus of 10350 sentences from news domain is used for training and the system is tested with 500 sentences. Using the proposed translation factors, the output of the translation quality is improved as indicated by the BLEU score and subjective evaluation.