Hunzalah Hassan Bhatti

Aslema at NADI 2026: Augmentation through Fewshot for SLU

Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges

This study reviews multi-turn conversational AI across text-only dialogue, AudioLLMs and speech-native systems, multimodal and omni-modal systems, and tool-augmented agents, and concludes with a research agenda for systems that can remember, revise, ground, speak, listen, act, and adapt across turns, modalities, and cultures.

ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation

An overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation is presented, covering spoken visual question answering and image-grounded hallucination detection in English and Modern Standard Arabic, and CRAI-Bench, evaluating the cultural accuracy of text-to-image generation.

Aslema at NADI 2026: Data Augmentation for Intent Recognition and Slot Filling

This work evaluates four omni LLMs in a zero-shot setting and shows that fine-tuning consistently outperforms zero-shot inference, and explores synthetic data augmentation by using an LLM to generate culturally grounded Tunisian Derja utterances, followed by voice cloning to generate synthetic speech.