Syllable-based Myanmar-English Neural Machine Translation

Abstract

The paper presents the first large scale evaluation of the quality of Syllable-based Neural Machine Translation (Syllable-NMT) system for Myanmar-English pair. Neural Machine Translation (NMT) system has reached state-of-the-arts results on some languages. However, one of the main challenges that NMT still faces is dealing with very large vocabularies and morphologically rich languages. Like other low-resources languages, Myanmar Language has a lots of morphology information. This issue lead is to increase the ambiguity and to decrease the quality of translation results. Moreover, rule-based and phrase-based techniques were used in the existing research on Myanmar translation with the small amount of parallel corpus. Therefore, a large amount of parallel corpus is prepared and introduces a NMT model that maps a source syllable sequence to a target word sequences to address the morphological problems. In addition, this paper shows some experiments results and compare them. Our results show that syllable-NMT system is able to surpass than the character-based and word-based NMT systems by 5 BLEU.