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Neural Machine TranslationNeural Machine Translation Research • April 11th, 2019
This work explores neural machine trans- lation between Myanmar (Burmese) and Rakhine (Arakanese). Rakhine is a lan- guage closely related to Myanmar, often considered a dialect. We implemented three prominent neural machine transla- tion (NMT) systems: recurrent neural networks (RNN), transformer, and con- volutional neural networks (CNN). The systems were evaluated on a Myanmar- Rakhine parallel text corpus developed by us. In addition, two types of word seg- mentation schemes for word embeddings were studied: Word-BPE and Syllable- BPE segmentation. Our experimental re- sults clearly show that the highest quality NMT and statistical machine translation (SMT) performances are obtained with Syllable-BPE segmentation for both types of translations. If we focus on NMT, we find that the transformer with Word-BPE segmentation outperforms CNN and RNN for both Myanmar-Rakhine and Rakhine- Myanmar translation. However, CNN with Syllable-BPE segmentation obtains a higher score than the RNN an
