[2605.06886] TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP
[Submitted on 7 May 2026]
Title:TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP
View a PDF of the paper titled TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP, by Mullosharaf K. Arabov
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Abstract:This work introduces TajPersLexon, a curated Tajik--Persian parallel lexical resource of 40,112 word and short-phrase pairs for cross-script lexical retrieval, transliteration, and alignment in low-resource settings. We conduct a comprehensive CPU-only benchmark comparing three methodological families: (i) a lightweight hybrid pipeline, (ii) neural sequence-to-sequence models, and (iii) retrieval methods. Our evaluation establishes that the task is essentially solvable, with neural and retrieval baselines achieving 98-99% top-1 accuracy. Crucially, we demonstrate that while large multilingual sentence transformers fail on this exact lexical matching, our interpretable hybrid model offers a favorable accuracy-efficiency trade-off for practical applications, achieving 96.4% accuracy in an OCR post-correction task. All experiments use fixed random seeds for full reproducibility. The dataset, code, and models will be publicly released.
Comments: Published in The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family (SilkRoadNLP 2026), pages 29-37, Rabat, Morocco. Association for Computational Linguistics
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.06886 [cs.CL]
(or arXiv:2605.06886v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.06886
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family (SilkRoadNLP 2026), pages 29-37
Related DOI:
https://doi.org/10.18653/v1/2026.silkroadnlp-1.4
DOI(s) linking to related resources
Submission history
From: Mullosharaf Arabov Am [view email] [v1] Thu, 7 May 2026 19:37:34 UTC (30 KB)
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