# IndoNLP 2025: Shared Task on Real-Time Reverse Transliteration for Romanized Indo-Aryan languages

arXiv: https://arxiv.org/abs/2501.05816 · HTML: https://mlsanity.com/paper/2501.05816/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [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)
- [Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users](https://mlsanity.com/paper/2205.03018/) (2022)
- [Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches](https://mlsanity.com/paper/2501.00529/) (2024)
- [A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation](https://mlsanity.com/paper/2403.16129/) (2024)
- [Decoding the Diversity: A Review of the Indic AI Research Landscape](https://mlsanity.com/paper/2406.09559/) (2024)
- [Swa Bhasha: Message-Based Singlish to Sinhala Transliteration](https://mlsanity.com/paper/2404.13350/) (2024)
- [Romanized to Native Malayalam Script Transliteration Using an Encoder-Decoder Framework](https://mlsanity.com/paper/2412.09957/) (2024)

## Built on by (most cited first, 3 of 3)

- [Modeling Romanized Hindi and Bengali: Dataset Creation and Multilingual LLM Integration](https://mlsanity.com/paper/2511.22769/) (2025)
- [Beyond Specialization: Benchmarking LLMs for Transliteration of Indian Languages](https://mlsanity.com/paper/2505.19851/) (2025)
- [Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resources](https://mlsanity.com/paper/2507.09245/) (2025)

## Abstract (arXiv)

The paper overviews the shared task on Real-Time Reverse Transliteration for Romanized Indo-Aryan languages. It focuses on the reverse transliteration of low-resourced languages in the Indo-Aryan family to their native scripts. Typing Romanized Indo-Aryan languages using ad-hoc transliterals and achieving accurate native scripts are complex and often inaccurate processes with the current keyboard systems. This task aims to introduce and evaluate a real-time reverse transliterator that converts Romanized Indo-Aryan languages to their native scripts, improving the typing experience for users. Out of 11 registered teams, four teams participated in the final evaluation phase with transliteration models for Sinhala, Hindi and Malayalam. These proposed solutions not only solve the issue of ad-hoc transliteration but also empower low-resource language usability in the digital arena.