Sinhala-Transliterator

This is the official codebase for the paper. Here we introduce Transliteration Solutions for Sinhala Language which is extendable for other Languages as well.

Introduction

We introduce two Transliteration solutions for Sinhala Transliteration problem as our solution for the Shared Task of IndoNLPWorkshop 2025.

Our Approaches

We introduced and evaluated two approaches as our solution for the task.

Rule-based Approach:

A rule-based solution for Sinhala Transliteration which the resources can be found in rule-based-method/ directory.

Seq2Seq Modelling Approach:

A deep-learning-based Seq2seq modeling solution for Sinhala Transliteration which the resources can be found in seq2seq-method/ directory. Our trained model is available on huggingface.

Results

• Official Test sets: https://github.com/IndoNLP-Workshop/IndoNLP-2025-Shared-Task (we have cleaned and arranged the official test sets into both csv and txt formats which can be found in test-sets/ directory)
• Test set 1 consists of 10,000 datapoints with General Romanized Typing Patterns and, Test set 2 consists of 5,000 datapoints with Adhoc Romanized Typing Patterns
• Following table shows the evaluation scores of our two approaches on the official test-sets

paper-url

Publication

If you are willing to use this work, please be kind enough to cite the following paper. Also don't hesitate to rate this repository if you find it useful.

@inproceedings{de-mel-etal-2025-sinhala,
    title = "{S}inhala Transliteration: A Comparative Analysis Between Rule-based and {S}eq2{S}eq Approaches",
    author = "De Mel, Widanalage Mario Yomal  and
      Wickramasinghe, Kasun Imesha  and
      de Silva, Nisansa  and
      Ranathunga, Surangika Dayani",
    editor = "Weerasinghe, Ruvan  and
      Anuradha, Isuri  and
      Sumanathilaka, Deshan",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages",
    month = jan,
    year = "2025",
    address = "Abu Dhabi",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.indonlp-1.19/",
    pages = "166--173",
    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."
}