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        <title>HACS-TL: Cross-Script Transfer Learning for Hausa Ajami Hate Speech Detection Using Transformer-Based Architecture</title>
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        <namePart type="given">Abdulkadir</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Ismail</namePart>
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            <title>Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script</title>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>The Arabic-derived scripts contain several languages that face challenges with the limited resources of speech detection, these challenges are worsened by the scarcity of resources and highly complex linguistic challenges. We proposed ( HACS-TL Hausa Ajami Cross-Script Transfer Learning) a brand new transformer-based architecture that focuses on the detection of hate speech within Ajami script. Hausa is a Chadic language which contains over 77 million speakers located in West Africa; it uses two types of scripts: the Latin (Boko) and the Arabic-derived Ajami which creates new computational difficulties. Our method combines scripts of artistically converted linguistics, augmented cross script multi-head attention, and dialect feature extraction to trellis the morphophonological depth of the Hausa. After a thorough examination using stratified cross-validation along with systemically augmented data, HACS-TL obtained a Macro F1 score of 76.09% which is a significant improvement from the other multilingual baselines (mBERT (69.17 % ) XLM-RoBERTa (73.20 % ) AraBERT (58.63% ) ) HACS-TL outperformed all of the previously stated models. Strong multilingual baselines refer to the other stated models; AraBERT (58.63) XLM-RoBERTa (73.20) mBERT (69.17) HACS-TL 70.73 + 10 % Cross-Script+ (mBERT) 46.73 + 0.9 % Cross-Script + AraBERT. The importance of cross-script attention and learning from transfer sources of resources to languages with limited scripts has proven effective. Our systematic method has aided the advancement of Arabic script homage Hausa and African language resources for the NLP of the Nubians in learning African languages and the intricate Nubian and cross-learning systems from different scripts.</abstract>
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        <date>2026-03</date>
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            <start>287</start>
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%0 Conference Proceedings
%T HACS-TL: Cross-Script Transfer Learning for Hausa Ajami Hate Speech Detection Using Transformer-Based Architecture
%A Bichi, Abdulkadir Shehu
%A Ali, Muqaddar
%A Sharma, Prashant
%A Abubakar, Ismail Dauda
%S Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F bichi-etal-2026-hacs
%X The Arabic-derived scripts contain several languages that face challenges with the limited resources of speech detection, these challenges are worsened by the scarcity of resources and highly complex linguistic challenges. We proposed ( HACS-TL Hausa Ajami Cross-Script Transfer Learning) a brand new transformer-based architecture that focuses on the detection of hate speech within Ajami script. Hausa is a Chadic language which contains over 77 million speakers located in West Africa; it uses two types of scripts: the Latin (Boko) and the Arabic-derived Ajami which creates new computational difficulties. Our method combines scripts of artistically converted linguistics, augmented cross script multi-head attention, and dialect feature extraction to trellis the morphophonological depth of the Hausa. After a thorough examination using stratified cross-validation along with systemically augmented data, HACS-TL obtained a Macro F1 score of 76.09% which is a significant improvement from the other multilingual baselines (mBERT (69.17 % ) XLM-RoBERTa (73.20 % ) AraBERT (58.63% ) ) HACS-TL outperformed all of the previously stated models. Strong multilingual baselines refer to the other stated models; AraBERT (58.63) XLM-RoBERTa (73.20) mBERT (69.17) HACS-TL 70.73 + 10 % Cross-Script+ (mBERT) 46.73 + 0.9 % Cross-Script + AraBERT. The importance of cross-script attention and learning from transfer sources of resources to languages with limited scripts has proven effective. Our systematic method has aided the advancement of Arabic script homage Hausa and African language resources for the NLP of the Nubians in learning African languages and the intricate Nubian and cross-learning systems from different scripts.
%U https://aclanthology.org/2026.abjadnlp-1.36/
%P 287-294

Markdown (Informal)

[HACS-TL: Cross-Script Transfer Learning for Hausa Ajami Hate Speech Detection Using Transformer-Based Architecture](https://aclanthology.org/2026.abjadnlp-1.36/) (Bichi et al., AbjadNLP 2026)

• HACS-TL: Cross-Script Transfer Learning for Hausa Ajami Hate Speech Detection Using Transformer-Based Architecture (Bichi et al., AbjadNLP 2026)

ACL

• Abdulkadir Shehu Bichi, Muqaddar Ali, Prashant Sharma, and Ismail Dauda Abubakar. 2026. HACS-TL: Cross-Script Transfer Learning for Hausa Ajami Hate Speech Detection Using Transformer-Based Architecture. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 287–294, Rabat, Morocco. Association for Computational Linguistics.