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

TL;DR

This paper develops a Bengali to Assamese statistical machine translation system using Moses, leveraging a parallel corpus and existing tools, aiming to improve machine translation for Indian languages.

Contribution

It presents a novel Bengali to Assamese SMT model using Moses with a specific corpus and tools, addressing a gap in Indian language machine translation research.

Findings

Created a Bengali-Assamese translation model using Moses

Utilized a 17,100 sentence parallel corpus for training

Addresses the lack of statistical MT research for Indian languages

Abstract

Machine dialect interpretation assumes a real part in encouraging man-machine correspondence and in addition men-men correspondence in Natural Language Processing (NLP). Machine Translation (MT) alludes to utilizing machine to change one dialect to an alternate. Statistical Machine Translation is a type of MT consisting of Language Model (LM), Translation Model (TM) and decoder. In this paper, Bengali to Assamese Statistical Machine Translation Model has been created by utilizing Moses. Other translation tools like IRSTLM for Language Model and GIZA-PP-V1.0.7 for Translation model are utilized within this framework which is accessible in Linux situations. The purpose of the LM is to encourage fluent output and the purpose of TM is to encourage similarity between input and output, the decoder increases the probability of translated text in target language. A parallel corpus of 17100 sentences in Bengali and Assamese has been utilized for preparing within this framework. Measurable MT procedures have not so far been generally investigated for Indian dialects. It might be intriguing to discover to what degree these models can help the immense continuous MT deliberations in the nation.