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Neural machine translation is a new approach to machine translation that has shown the effective results for high-resource languages. Recently, the attention-based neural machine translation with the large scale parallel corpus plays an important role to achieve high performance for translation results. In this research, a parallel corpus for Myanmar-English language pair is prepared and attention-based neural machine translation models are introduced based on word to word level, character to word level, and syllable to word level. We do the experiments of the proposed model to translate the long sentences and to address morphological problems. To decrease the low resource problem, source side monolingual data are also used. So, this work investigates to improve Myanmar to English neural machine translation system. The experimental results show that syllable to word level neural mahine translation model obtains an improvement over the baseline systems.

Attention-Based Syllable Level Neural Machine Translation System for Myanmar to English Language Pair