# Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches

arXiv: https://arxiv.org/abs/2501.00529 · HTML: https://mlsanity.com/paper/2501.00529/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 31 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://mlsanity.com/paper/2004.11867/) (2020)
- [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://mlsanity.com/paper/1911.04944/) (2019)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset](https://mlsanity.com/paper/2007.01176/) (2020)
- [Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?](https://mlsanity.com/paper/2203.08850/) (2022)
- [Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora](https://mlsanity.com/paper/2402.07446/) (2024)
- [Sequence-to-sequence neural network models for transliteration](https://mlsanity.com/paper/1610.09565/) (2016)
- [Swa Bhasha: Message-Based Singlish to Sinhala Transliteration](https://mlsanity.com/paper/2404.13350/) (2024)
- [SiTSE: Sinhala Text Simplification Dataset and Evaluation](https://mlsanity.com/paper/2412.01293/) (2024)

## Built on by (most cited first, 6 of 6)

- [IndoNLP 2025: Shared Task on Real-Time Reverse Transliteration for Romanized Indo-Aryan languages](https://mlsanity.com/paper/2501.05816/) (2025)
- [SiDiaC: Sinhala Diachronic Corpus](https://mlsanity.com/paper/2509.17912/) (2025)
- [SiDiaC-v.2.0: Sinhala Diachronic Corpus Version 2.0](https://mlsanity.com/paper/2603.10861/) (2026)
- [Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resources](https://mlsanity.com/paper/2507.09245/) (2025)
- [How Good is BLI as an Alignment Measure: A Study in Word Embedding Paradigm](https://mlsanity.com/paper/2511.13040/) (2025)
- [Script Sensitivity: Benchmarking Language Models on Unicode, Romanized and Mixed-Script Sinhala](https://mlsanity.com/paper/2601.14958/) (2026)

## Abstract (arXiv)

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/