arXiv AI
Wazobia Eval: A Benchmark for Nigerian Pidgin Emotion Understanding, Sarcasm Detection, and Cultural Reasoning
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arXiv AI
Jun 19
The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
arXiv:2606. 20255v1 Announce Type: cross Abstract: We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent.
arXiv AI
Aug 17
Jais 2: A Family of Arabic-Centric Open Large Language Models
arXiv:2608. 13580v1 Announce Type: cross Abstract: Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report.
arXiv Computation and Language
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Why Current XAI Is Not Enough for Arabic NLP: A Critical Survey of the Explainability Gap
The paper surveys the state of Explainable AI (XAI) in Arabic NLP, highlighting three gaps: a method gap where Arabic XAI relies mainly on limited post‑hoc techniques; a task gap with most work focused on classification tasks and little on generation, retrieval, or dialogue; and a linguistic gap where explanations rarely address Arabic‑specific phenomena such as morphology, dialects, and diglossia. It proposes a taxonomy of tasks, methods, linguistic units, and evaluation practices, and outlines a research agenda for linguistically grounded Arabic XAI.
arXiv AI
Jun 29
Do Speech Emphasis Models Generalize across Languages and Emotions?
arXiv:2606. 27717v1 Announce Type: cross Abstract: Prosodic emphasis varies across languages, emotions, and speaking styles, yet existing emphasis detection models are largely trained and evaluated on monolingual neutral read speech.
arXiv AI
Aug 12
Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse
arXiv:2608. 10810v1 Announce Type: cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions.
Hugging Face Trending Papers
Jul 29
AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes
Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets. While hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or coarse harmful-content labels.