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://github.com/merchantrayyan/ParsTranslit.

Despite speaking dialects of the same language, Persian speakers from Tajikistan cannot read Persian texts from Iran and Afghanistan. This is due to the fact that Tajik Persian is written in the Tajik-Cyrillic script, while Iranian and Afghan Persian are written in the Perso-Arabic script. As the formal registers of these dialects all maintain high levels of mutual intelligibility with each other, machine transliteration has been proposed as a more practical and appropriate solution than machine translation. Unfortunately, Persian texts written in both scripts are much more common in print in Tajikistan than online. This paper introduces a novel corpus meant to remedy that gap: ParsText. ParsText contains 2,813 Persian sentences written in both Tajik-Cyrillic and Perso-Arabic manually collected from blog pages and news articles online. This paper presents the need for such a corpus, previous and related work, data collection and alignment procedures, corpus statistics, and discusses directions for future work.