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        <title>TajPersLexon: A Tajik–Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP</title>
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        <namePart type="given">Mullosharaf</namePart>
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            <title>The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family</title>
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            <namePart type="given">Rayyan</namePart>
            <namePart type="family">Merchant</namePart>
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            <namePart type="given">Karine</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T TajPersLexon: A Tajik–Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP
%A Arabov, Mullosharaf Kurbonovich
%Y Merchant, Rayyan
%Y Megerdoomian, Karine
%S The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-371-5
%F arabov-2026-tajperslexon
%X 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.
%U https://aclanthology.org/2026.silkroadnlp-1.4/
%P 29-37

Markdown (Informal)

[TajPersLexon: A Tajik–Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP](https://aclanthology.org/2026.silkroadnlp-1.4/) (Arabov, SilkRoadNLP 2026)

• TajPersLexon: A Tajik–Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP (Arabov, SilkRoadNLP 2026)

ACL

• Mullosharaf Kurbonovich Arabov. 2026. TajPersLexon: A Tajik–Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP. In The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family, pages 29–37, Rabat, Morocco. Association for Computational Linguistics.