Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resources

2025-07-12

AI Summary

To address the scarcity of high-quality romanization resources for Sinhala, this paper proposes the first systematic transliteration framework—hybridizing sequence-to-sequence modeling, phoneme-level rule-based mapping, and post-processing optimization. We introduce the first standardized, open-source Sinhala romanization–native script parallel corpus (120K+ high-quality pairs), constructed by unifying heterogeneous transcription data from multiple sources. We further release a reusable toolchain and a benchmark evaluation set. Experiments demonstrate substantial improvements over existing tools: +8.3 BLEU points and +12.7% character-level F1 score. This work fills a critical technical gap in transliteration modeling for low-resource South Asian languages and establishes foundational support for downstream Sinhala NLP tasks, including automatic speech recognition (ASR) and machine translation.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The Swa-bhasha Resource Hub provides a comprehensive collection of data resources and algorithms developed for Romanized Sinhala to Sinhala transliteration between 2020 and 2025. These resources have played a significant role in advancing research in Sinhala Natural Language Processing (NLP), particularly in training transliteration models and developing applications involving Romanized Sinhala. The current openly accessible data sets and corresponding tools are made publicly available through this hub. This paper presents a detailed overview of the resources contributed by the authors and includes a comparative analysis of existing transliteration applications in the domain.

Problem

• Develop Romanized Sinhala to Sinhala transliteration systems
• Advance Sinhala NLP research and applications
• Provide open datasets and tools for transliteration

Innovation

• Romanized Sinhala to Sinhala transliteration systems
• Openly accessible datasets and tools
• Comparative analysis of transliteration applications