Reordering Model with Recurrent Neural Networks for Myanmar- English Statistical Machine Translation

Abstract

Word reordering is a problematic issue forlanguage pairs with significantly different word orders,such as the translation between a subject-verb-object(SVO) language and a subject-object-verb (SOV)language. When translating between language pairswith high disparity in word order, reordering isextremely desirable for translation accuracy. In thispaper, the future research directions of reorderingmodels for Myanmar-English statistical machinetranslation (SMT) are also depicted. In this reorderingmodel, the word order on source-side is arranged intothe target side word order, before SMT system isapplied. We propose the use of recurrent neuralnetworks (RNNs) to model preordering for SMT