mlsanityData to 7 Oct 2026

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

arXiv 2501.00529 ↗First posted 31 Dec 2024cs.CLcited by 6

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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/

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 31 Dec 2024.

Builds on

18 of 40 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  5. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  6. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  7. 2020Beyond English-Centric Multilingual Machine Translationcited by 559
  8. 2020Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translationcited by 277
  9. 2019CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEBcited by 177
  10. 2019CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairscited by 136
  11. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 108
  12. 2019Survey on Publicly Available Sinhala Natural Language Processing Tools and Researchcited by 45
6 more
  1. 2020Processing South Asian Languages Written in the Latin Script: the Dakshina Datasetcited by 35
  2. 2022Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?cited by 24
  3. 2024Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corporacited by 13
  4. 2016Sequence-to-sequence neural network models for transliterationcited by 7
  5. 2024Swa Bhasha: Message-Based Singlish to Sinhala Transliterationcited by 3
  6. 2024SiTSE: Sinhala Text Simplification Dataset and Evaluationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025IndoNLP 2025: Shared Task on Real-Time Reverse Transliteration for Romanized Indo-Aryan languagescited by 3
  2. 2025SiDiaC: Sinhala Diachronic Corpuscited by 2
  3. 2026SiDiaC-v.2.0: Sinhala Diachronic Corpus Version 2.0cited by 1
  4. 2025Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resourcescited by 0
  5. 2025How Good is BLI as an Alignment Measure: A Study in Word Embedding Paradigmcited by 0
  6. 2026Script Sensitivity: Benchmarking Language Models on Unicode, Romanized and Mixed-Script Sinhalacited by 0