Due to reasons of convenience and lack of tech literacy, transliteration (i.e., Romanizing native scripts instead of using localization tools) is eminently prevalent in the context of low-resource languages such as Sinhala, which have their own writing script. In this study, our focus is on Romanized Sinhala transliteration. We propose two methods to address this problem: Our baseline is a rule-based method, which is then compared against our second method where we approach the transliteration problem as a sequence-to-sequence task akin to the established Neural Machine Translation (NMT) task. For the latter, we propose a Transformer-based Encode-Decoder solution. We witnessed that the Transformer-based method could grab many ad-hoc patterns within the Romanized scripts compared to the rule-based method. The code base associated with this paper is available on GitHub - https://github.com/kasunw22/Sinhala-Transliterator/
Cite
@article{arxiv.2501.00529,
title = {Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches},
author = {Yomal De Mel and Kasun Wickramasinghe and Nisansa de Silva and Surangika Ranathunga},
journal= {arXiv preprint arXiv:2501.00529},
year = {2025}
}