# Connecting the Persian-speaking World through Transliteration

arXiv: https://arxiv.org/abs/2502.20047 · HTML: https://mlsanity.com/paper/2502.20047/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Applying the Transformer to Character-level Transduction](https://mlsanity.com/paper/2005.10213/) (2020)
- [Transformer based Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/2004.06338/) (2020)
- [A Rule-based Kurdish Text Transliteration System](https://mlsanity.com/paper/1811.10278/) (2018)
- [Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging](https://mlsanity.com/paper/2009.09474/) (2020)

## Built on by (most cited first, 4 of 4)

- [ParsTranslit: Truly Versatile Tajik-Farsi Transliteration](https://mlsanity.com/paper/2510.07520/) (2025)
- [TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP](https://mlsanity.com/paper/2605.06886/) (2026)
- [Character-Level Transformer for Tajik-Persian Transliteration with a Parallel Lexical Corpus](https://mlsanity.com/paper/2605.09092/) (2026)
- [TajikNLP: An Open-Source Toolkit for Comprehensive Text Processing of Tajik (Cyrillic Script)](https://mlsanity.com/paper/2605.04583/) (2026)

## Abstract (arXiv)

Despite speaking mutually intelligible varieties of the same language, speakers of Tajik Persian, written in a modified Cyrillic alphabet, cannot read Iranian and Afghan texts written in the Perso-Arabic script. As the vast majority of Persian text on the Internet is written in Perso-Arabic, monolingual Tajik speakers are unable to interface with the Internet in any meaningful way. Due to overwhelming similarity between the formal registers of these dialects and the scarcity of Tajik-Farsi parallel data, machine transliteration has been proposed as more a practical and appropriate solution than machine translation. This paper presents a transformer-based G2P approach to Tajik-Farsi transliteration, achieving chrF++ scores of 58.70 (Farsi to Tajik) and 74.20 (Tajik to Farsi) on novel digraphic datasets, setting a comparable baseline metric for future work. Our results also demonstrate the non-trivial difficulty of this task in both directions. We also provide an overview of the differences between the two scripts and the challenges they present, so as to aid future efforts in Tajik-Farsi transliteration.