Hierarchical Markov based Reordering Model for English-Myanmar Translation

Abstract

In statistical machine translation, reordering is crucial component for translation of the different languages with different word orders. Without reordering during language translation, sentences can only be translated properly into a language with similar word order. An effective reordering scheme is essential to model translation between languages with different word orders, such as SVO-languages (English or Chinese) and SOVlanguages (Japanese or Turkish or Myanmar). In this paper, we focus on reorder scheme for lexical reordering to incorporate into our translation model. If a sentence is given, our proposed reordering system performs three tasks on this sentence such as word level reordering, chunk level reordering and clause level reordering. In this research, Markov based Reordering Model will be implemented. Moreover, the movement parameters to input the reordering model are taken from the rules automatically generated from parallel aligned corpus.