Code-mixed and Romanized texts are widely used in digital content, yet they remain largely underexplored for many low-resource languages, including Sinhala. The scarcity of high-quality parallel data has limited progress on downstream tasks, such as machine translation and transliteration. We introduce SinMix2Mono, the largest manually annotated parallel training dataset, followed by the first gold standard benchmark and code-mixed transliteration ambiguity corpora for code-mixed romanized Sinhala to Sinhala conversion. The dataset comprises approximately 25,000 real-world sentences collected from social media, covering diverse domains and authentic code-mixing patterns. To ensure high-quality translations, we used an annotation pipeline that combined rule-based transliteration, LLM-assisted translation, and human validation. The golden test dataset, which includes 2549 sentences, and the code-mixed transliteration ambiguity test were validated by three annotators, yielding Gwet’s AC1 scores of 0.7465 and 0.7068, respectively. We benchmarked nine systems, including statistical, neural and commercial LLMs. SinMix2Mono provides a robust training and evaluation resource, establishing a strong benchmark for future research on Sinhala code-mixed translation and transliteration.

In multilingual societies, it is common to observe the blending of multiple languages in communication, a phenomenon known as Code-mixing. Globalization and the increasing influence of social media have further amplified multilingualism, resulting in a wider use of code-mixing. This systematic review analyzes existing translation and transliteration techniques for code-mixed Indo-Aryan languages, spanning rule-based and statistical approaches to neural machine translation and transformer-based architectures. It also examines publicly available code-mixed datasets designed for machine translation and transliteration tasks, along with the evaluation metrics commonly introduced and applied in prior studies. Finally, the paper discusses current challenges and limitations, highlighting future research directions for developing more tailored translation pipelines for code-mixed Indo-Aryan languages.