Myanmar-English Bidirectional Machine Translation System with Numerical Particles Identification

This paper the development of MyanmarEnglish bidirectional machine translation system is implemented applying Rule based machine translation approach. Stanford and ML2KR parsers are used for preprocessing step. From this step, parsers generate corresponding parse tree structures. Used parsers generate corresponding CFG rules which are collected and created as synchronous context free grammar SCFG rules. Myanmar language can be written free order style, but it must be verb final structure. Therefore, CFG rules are required for reordering the structure of the two languages. After that tree to tree transformation is carried on the source tree structure which corresponds with used parser (Stanford parser or ML2KR‟s parser). When source parse tree is transformed as target parse tree, it is changed according to the SCFG rules. And then system carries out the morphological synthesis. In this stage, we need to solve only for English to Myanmar machine translation because Myanmar language is morphologically rich language. Therefore, particles for Myanmar language can be solved in this system by proposed algorithm. After finishing morphological synthesis, this system generates meaningful and appropriate smoothing sentences.