Anthology ID: 2020.lrec-1.364 Volume: Proceedings of the Twelfth Language Resources and Evaluation Conference Month: May Year: 2020 Address: Marseille, France Venue: LREC SIG: Publisher: European Language Resources Association Note: Pages: 2980–2983 Language: English URL: DOI: Bibkey: myat-mon-etal-2020-myanmar Cite (ACL): Aye Myat Mon, Chenchen Ding, Hour Kaing, Khin Mar Soe, Masao Utiyama, and Eiichiro Sumita. Different units used in the Myanmar script for processing were also compared and discussed. The neural network model outperformed the statistical model significantly in terms of the BLEU score on the character level. We evaluated the automatic transliteration performance using statistical and neural network-based approaches based on the prepared data. The data have been released under a CC BY-NC-SA license. In this study, we constructed a Myanmar-English named entity dictionary containing more than eighty thousand transliteration instances. For the Myanmar (Burmese) language, robust automatic transliteration for borrowed English words is a challenging task because of the complex Myanmar writing system and the lack of data. It is a crucial task for various downstream natural language processing applications. Abstract Transliteration is generally a phonetically based transcription across different writing systems.
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