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

dc.contributor.author	Nyein, May Kyi
dc.contributor.author	Soe, Khin Mar
dc.date.accessioned	2019-07-15T04:52:03Z
dc.date.available	2019-07-15T04:52:03Z
dc.date.issued	2017-02-16
dc.identifier.uri	http://onlineresource.ucsy.edu.mm/handle/123456789/894
dc.description.abstract	Word reordering is a problematic issue for language 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 pairs with high disparity in word order, reordering is extremely desirable for translation accuracy. In this paper, the future research directions of reordering models for Myanmar-English statistical machine translation (SMT) are also depicted. In this reordering model, the word order on source-side is arranged into the target side word order, before SMT system is applied. We propose the use of recurrent neural networks (RNNs) to model preordering for SMT.	en_US
dc.language.iso	en	en_US
dc.publisher	Fifteenth International Conference on Computer Applications (ICCA 2017)	en_US
dc.subject	Reordering	en_US
dc.subject	English-Myanmar statistical machine translation	en_US
dc.subject	recurrent neural networks	en_US
dc.title	Reordering Model with Recurrent Neural Networks for Myanmar- English Statistical Machine Translation	en_US
dc.type	Article	en_US