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Improving English-to-Myanmar Statistical Machine Translation by using Recurrent Neural Network Language Model and Hierarchical Reordering
http://hdl.handle.net/20.500.12678/0000004996
http://hdl.handle.net/20.500.12678/0000004996e34c4d30-5e02-4748-acd0-992ce9fb6548
efbb6c51-3b86-447a-8102-0fd4780f3f12
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Title | Improving English-to-Myanmar Statistical Machine Translation by using Recurrent Neural Network Language Model and Hierarchical Reordering | |||||
Language | en | |||||
Publication date | 2017-02-16 | |||||
Authors | ||||||
Zin, May Myo | ||||||
Soe, Khin Mar | ||||||
Description | ||||||
In this paper, a comparison between neuralbased network that adopts Recurrent Neural Network(RNN) language model and statistical based one withN-gram language model is conducted for English-to-Myanmar phrase-based statistical machine translation(PBSMT). In this comparison, lexicalized reorderingmodels such as word-based, phrase-based andhierarchical orientation models are used as anadditional reordering model to investigate the overallperformance of PBSMT. The perplexity valueevaluation of both language models showed that theuse of RNN obtains a more excellent result. Accordingto the obtained BLEU and RIBES scores andadditional human visual inspection, the English-to-Myanmar PBSMT with RNN language model andhierarchical reordering model is the best one in termsof improving adequacy and fluency. | ||||||
Keywords | ||||||
Phrase-based SMT, RNN, Hierarchical Reordering | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/893 | |||||
Journal articles | ||||||
Fifteenth International Conference on Computer Applications(ICCA 2017) | ||||||
Conference papers | ||||||
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Thesis/dissertations |