Implementation of Translation Model for Statistical Myanmar-English Translation

Abstract

Machine Translation is defined as the task of transforming an existing text written in a source language, into an equivalent text in a different language, the target language. The statistical machine translation approach uses two types of information: a language model and a translation model. In this paper, translation model is based on the noisy channel model. We use Bayes rule to reformulate the translation probability for translating a foreign sentence f into English sentence e. we use N-gram language model. In machine translation, reordering is requiring to reorder target phrases since different languages have different word order requirements. In this paper, we present Chunks-based reordering approach to reorder target phrases. Reordering rules are automatically generated by using bilingual corpus. The goal of this paper is to improve the translation performance for a Statistical Machine Translation of Myanmar to English.