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 Computation and Language
Oct 5
Mawqif-XT: An Arabic Benchmark Dataset for Cross-Target Stance Detection
The paper introduces Mawqif-XT, a new Arabic benchmark dataset comprising 996 manually annotated tweets from three public targets: Women Driving, E-Cars, and Trimester System. Each tweet is labeled for stance, sentiment, and sarcasm following the Mawqif annotation scheme, and the dataset is intended as a held‑out evaluation set to test cross‑target generalization. Baseline results are provided using Arabic and multilingual transformer models as well as zero‑shot large language models, enabling reproducible evaluation alongside the original Mawqif dataset.
arXiv Machine Learning
Sep 11
E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets
E-CONAN introduces Arabic textual entailment and natural inference benchmarks comprising two datasets: E-CONAN-2 (2-way RTE) and E-CONAN-3 (3-way NLI). The datasets are built from automatically-translated pairs, human-validated machine translations, hand-crafted pairs from Arabic teaching books, and rumor-containing news headlines. The authors evaluated nine multilingual pretrained models and five large language models on these benchmarks, demonstrating that E-CONAN offers a more diverse and robust assessment than existing datasets like XNLI and ArNLI.
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
Aug 28
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
Sep 1
EmoLASP: Emotion Recognition with Language Models and Answer Set Programming
arXiv:2608.29035v1 Announce Type: new Abstract: Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to pr...