# ParsTranslit: Truly Versatile Tajik-Farsi Transliteration

arXiv: https://arxiv.org/abs/2510.07520 · HTML: https://mlsanity.com/paper/2510.07520/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [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)
- [ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages using Wikidata](https://mlsanity.com/paper/2405.09496/) (2024)
- [GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains](https://mlsanity.com/paper/2411.00491/) (2024)
- [Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging](https://mlsanity.com/paper/2009.09474/) (2020)
- [Connecting the Persian-speaking World through Transliteration](https://mlsanity.com/paper/2502.20047/) (2025)

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

- [A Systematic Benchmark of Machine Transliteration Models for the Tajik-Farsi Language Pair: A Comparative Study from Rule-Based to Transformer Architectures](https://mlsanity.com/paper/2605.02270/) (2026)
- [Benchmarking Parameter-Efficient Fine-Tuning of Large Language Models for Low-Resource Tajik Text Generation with the Tajik Web Corpus](https://mlsanity.com/paper/2605.03742/) (2026)
- [Benchmarking POS Tagging for the Tajik Language: A Comparative Study of Neural Architectures on the TajPersParallel Corpus](https://mlsanity.com/paper/2605.04576/) (2026)
- [TajikNLP: An Open-Source Toolkit for Comprehensive Text Processing of Tajik (Cyrillic Script)](https://mlsanity.com/paper/2605.04583/) (2026)

## Abstract (arXiv)

As a digraphic language, the Persian language utilizes two written standards: Perso-Arabic in Afghanistan and Iran, and Tajik-Cyrillic in Tajikistan. Despite the significant similarity between the dialects of each country, script differences prevent simple one-to-one mapping, hindering written communication and interaction between Tajikistan and its Persian-speaking ``siblings''. To overcome this, previously-published efforts have investigated machine transliteration models to convert between the two scripts. Unfortunately, most efforts did not use datasets other than those they created, limiting these models to certain domains of text such as archaic poetry or word lists. A truly usable transliteration system must be capable of handling varied domains, meaning that suck models lack the versatility required for real-world usage. The contrast in domain between data also obscures the task's true difficulty. We present a new state-of-the-art sequence-to-sequence model for Tajik-Farsi transliteration trained across all available datasets, and present two datasets of our own. Our results across domains provide clearer understanding of the task, and set comprehensive comparable leading benchmarks. Overall, our model achieves chrF++ and Normalized CER scores of 87.91 and 0.05 from Farsi to Tajik and 92.28 and 0.04 from Tajik to Farsi. Our model, data, and code are available at https://anonymous.4open.science/r/ParsTranslit-FB30/.