Detecting hate speech in low-resource and unseen languages remains challenging due to limited labeled data and linguistic diversity. This paper presents a comparative study of zero-shot cross-lingual transfer for hate speech detection using two multilingual transformer models: mDeBERTa-v3 and XLM-RoBERTa. To the best of our knowledge, mDeBERTa-v3 has not been previously used by researchers for zero-shot cross-lingual hate speech detection, making this the first study to evaluate its capabilities in this task. Furthermore, we introduce new unseen languages that have not been studied before in this context, including Hebrew, Amharic, and Swahili, alongside other languages such as Indonesian, Danish Portuguese, Turkish, French, and Russian. We evaluate model performance under three training scenarios: a single source language (Turkish), semantically similar language clusters, and multiple clusters from different language families. Experimental results show that mDeBERTa-v3 consistently outperforms XLM-RoBERTa in zero-shot settings. The most notable improvement is observed for Hebrew, where the macro F1 score increases from 0.39 (XLM-RoBERTa) to 0.71 (mDeBERTa-v3), a gain of 0.32. Substantial gains are also seen for Amharic (0.52 → 0.73, +0.21), Indonesian (0.57 → 0.71, +0.14), and Swahili (0.65 → 0.75, +0.10). Across all experimental conditions, mDeBERTa-v3 achieves average macro F1 gains ranging from 0.04 to 0.19, with statistical significance (p < 0.02). The model’s advantage is attributed to its disentangled attention mechanism, which enables better generalization across typologically distant languages. These findings establish mDeBERTa-v3 as a novel and more robust architecture for zero-shot cross-lingual hate speech detection, particularly for previously unexplored low-resource languages.
Ghadeer Al-Badani, M. Alsurori, Akram Alsubari· 2026 6th International Confe...· 0 citations
This paper introduces MAAD, a high-quality, carefully constructed and curated by the authors large-scale Arabic dataset for abstractive news summarisation. The authors selected a high-quality subset of 50,000 articles from the dataset Original, which contains 602,792 articles. To maintain the quality, diversity, and training suitability of the subset, the subset underwent a multi-stage preprocessing pipeline involving noise removal, duplicate filtering, linguistic normalisation, and expert validation. The experimental evaluation was executed in two phases. In the first phase, three transformer-based models (ArabicT5, AraBART, and mT5) were evaluated on a controlled subset of 1,110 articles to establish fair baseline comparisons among models, where ArabicT5 achieved the best performance (ROUGE-1: 23.64, ROUGE-2: 11.82, ROUGE-L: 22.10). In the second phase, ArabicT5-base was trained on all 50,000 articles to evaluate scalability, achieving substantially improved results of 68.4, 52.3, and 64.1, respectively, with a BLEU score of 58.7. The findings emphasise the significance of scale, effective preprocessing, and the benefits of Arabic-specific pretraining on the quality of summarisation. Moreover, a human evaluation on 500 randomly sampled instances verified fluency and adequacy scores of 4.86 and 4.35, respectively, with a strong inter-annotator agreement (Cohen's Kappa: 0.78 and 0.74). Overall, the findings indicate that MAAD is a reliable and scalable dataset with strong potential to serve as a benchmark for Arabic abstractive summarisation and to support the development of robust transformer-based models.
M. Al-Nahari, Ayedh abdulaziz Mohsen, Nada Abdu Al-Humidi et al.· 2026 6th International Confe...· 0 citations
