S inhala Transliteration: A Comparative Analysis Between Rule-based and S eq2 S eq Approaches (2025)

Abstract: 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.

## introduction sinhala language, spoken by over 16 million people in sri lanka, presents unique challenges for computational processing due to its distinct script and structure @xcite . in modern-day digital communication, it is common to use singlish 1 , where sinhala (sinhalese) words are written with latin (english) script (liwera and @xcite . while the widespread use of singlish in informal communication calls for efficient transliteration systems capable of accurately converting it into the sinhala script, this task is made difficult by code-mixed and code-switched usage of singlish scripts @xcite . further, ad-hoc approximations are used by users when they approximate the abugida sinhala script @xcite using the latin script which is an alphabet @xcite . yet, we do not find sufficient transliteration research done for singlish. as for many nlp tasks, the early solutions for transliteration were based on rule-based techniques that relied on predefined character mappings @xcite . however, they often struggled when confronted with the variability in the format in which sinhala words were written using english script @xcite ). in contrast, deep learning models, especially transformerbased architectures @xcite , have proved to perform well for the transliteration task @xcite . however, such deep learning methods have not been used to implement transliteration systems related to sinhala. this paper introduces two distinct methods, a rule-based approach and a deep learning-based approach to solve the singlish to sinhala transliteration problem. the deep learning based transliteration system is implemented on a pretrained sequence-to-sequence multilingual language model, akin to a machine translation task. subsequently, we evaluate their effectiveness and limitations. according to our results, we observed that the deep learning approach is more robust to language variability compared to the rule-based approach. the rest of the sections will discuss the related work, our methodology, the results we obtained, and the conclusions. ## related work machine transliteration focuses on converting text from one script to another using phonetic or spelling equivalents, ideally mapping words or letters systematically between writing systems @xcite . ## results and discussion romanized script rule-based result dl-based result kmk nehe modyi wge ක්මක් ෙනෙහ ෙමාදයි වෙග කමක් නැහැ ෙමා් ඩයි වෙග් mta ehema denila ne eth eya uda thttuwe innkota klin ehema denila ne මට එෙහම ෙදනිල ෙන එත එය උඩ තටටුෙව් ඉන්න්ෙකාට ක්ලින් එෙහම ෙදනිල ෙන මට එෙහම දැනිලා නෑ ඒත් එයා උඩ තට්ටුෙව් ඉන්නෙකාට කලින් එෙහම දැනිලා නෑ eka nrkyi oya dnnwa mma adahas krna de එක න්රක්යි ඔය දන්න්ව මම අඩහස ක්රන ෙද ඒක නරකයි ඔයා දන්නවා මම අදහස් කරන ෙද් nevertheless, the efficiency concerned, the rulebased approach is much faster than the deep learning approach. in the cpu, the deep learning approach becomes extremely slow making it hard to use for real-time applications. in contrast on a gpu, we can achieve real-time performance for the deep learning approach as well. check ## conclusion we have experimented with two approaches for the transliteration task for romanized sinhala and english. the first approach is a rule-based statistical approach. the second approach addresses the transliteration task as a translation task using a pre-trained multilingual encoder-decoder language model. both approaches have their own pros and cons. when it comes to accuracy, the deep learning approach outperformed the rule-based method while in terms of efficiency, it is the other way around.