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

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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


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