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Unsupervised Neural Machine Translation between Myanmar Sign Language and Myanmar Language

http://hdl.handle.net/20.500.12678/0000007667
2ee137a8-23ba-47a9-ba3f-0015bb746d59
5ead0de3-62be-4d1e-ae77-71bf176dc8e9
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Unsupervised Unsupervised Neural Machine Translation between Myanmar Sign Language and Myanmar Language.pdf (1.4 MB)
Publication type
Journal article
Upload type
Publication
Title
Title Unsupervised Neural Machine Translation between Myanmar Sign Language and Myanmar Language
Language en
Publication date 2020-04-08
Authors
Swe Zin Moe
Ye Kyaw Thu
Hnin Aye Thant
Nandar Win Min
Thepchai Supnithi
Description
This paper investigate the utility of unsupervised Neural Machine translation (U-NMT) on low-resource language pairs: Myanmar sign language (MSL) and Myanmar language. Since state-ofthe-art neural machine translation (NMT) require large amount of parallel sentences, which we do not have for pairs we consider. We focus primarily on incorporating two different types of monolingual data: translated Myanmar sentences of primary English and myPOS data, only into our Myanmar language side. We found that the incorporating monolingual data achieved higher performance than the baseline approach. We prepared four types of training data for U-NMT models and the results clearly show that using the myPOS corpus on incorporating the Myanmar language monolingual data achieved the
highest BLEU scores when compared to other training data.
Keywords
Machine Translation, Neural Machine Translation, Unsupervised Neural Machine Translation, Myanmar sign language, Myanmar language
Journal articles
Issue 1
Unsupervised Neural Machine Translation between Myanmar Sign Language and Myanmar Language
Pages 53-61
Volume 4
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