Automatic Reordering Rule Generation and Application of Reordering Rules in Stochastic Reordering Model for EnglishMyanmar Machine Translation

Abstract

Reordering is one of the most challenging and important problems in Statistical Machine Translation. Without reordering capabilities, sentences can be translated correctly only in case when both languages implied in translation have a similar word order. When translating is between language pairs with high disparity in word order, word reordering is extremely desirable for translation accuracy improvement. Our Language, Myanmar is a verb final language and reordering is needed when our language is translated from other languages with different word orders. In this paper, automatic reordering rule generation and application of generated reordering rules in stochastic reordering model is presented. This work is intended to be incorporated into English-Myanmar Machine Translation system. In order to generate reordering rules; English-Myanmar parallel tagged aligned corpus is firstly created. Then reordering rules are generated automatically by using the linguistic information from this parallel tagged aligned corpus. In this paper, proposed function tag and part-of-speech tag reordering rule extraction algorithms are used to generate reordering rule automatically and First Order Markov theory is applied to implement stochastic reordering model.