ParsTranslit: Truly Versatile Tajik-Farsi Transliteration
This work addresses the challenge of written communication between Tajikistan and other Persian-speaking countries, which is incremental as it builds on existing transliteration efforts but improves versatility and benchmarking.
The paper tackles the problem of transliteration between Tajik-Cyrillic and Perso-Arabic scripts for Persian dialects, which is hindered by script differences and limited domain coverage in prior models. It presents a new state-of-the-art sequence-to-sequence model that achieves chrF++ scores of 87.91 and 92.28 and Normalized CER scores of 0.05 and 0.04 for Farsi-to-Tajik and Tajik-to-Farsi directions, respectively, across varied domains.
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/.