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Machine dialect interpretation assumes a real part in encouraging man-machinecorrespondence and in addition men-men correspondence in Natural LanguageProcessing (NLP). Machine Translation (MT) alludes to utilizing machine tochange one dialect to an alternate. Statistical Machine Translation is a typeof MT consisting of Language Model (LM), Translation Model (TM) and decoder. Inthis paper, Bengali to Assamese Statistical Machine Translation Model has beencreated by utilizing Moses. Other translation tools like IRSTLM for LanguageModel and GIZA-PP-V1.0.7 for Translation model are utilized within thisframework which is accessible in Linux situations. The purpose of the LM is toencourage fluent output and the purpose of TM is to encourage similaritybetween input and output, the decoder increases the probability of translatedtext in target language. A parallel corpus of 17100 sentences in Bengali andAssamese has been utilized for preparing within this framework. Measurable MTprocedures have not so far been generally investigated for Indian dialects. Itmight be intriguing to discover to what degree these models can help theimmense continuous MT deliberations in the nation.

Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based)