Neural Named Entity Transliteration for Myanmar to English Language Pair

Abstract

Named entity (NE) transliteration is mainly a phonetically based transcription of names across languages using different writing systems.This is a crucial task for various downstream natural language processing applications, such as information retrieval, machine translation, automatic speech recognition and so on.Robust transliteration of named entities is still a challenging task for Myanmar language because of the complex writing system and the lack of data.In this paper, we proposed our Myanmar-English named entity terminology dictionary and experimented on transformer-based neural network model.Furthermore, we evaluated the performance of neural network-based approach on the transliteration tasks using BLEU score.Different units in the Myanmar script, i.e., character units, sub-syllable units and syllables units are compared in the experiments.