Aslema at NADI 2026: Augmentation through Fewshot for SLU
Tajwaar Shafiq, Hunzalah Hassan Bhatti, S. Chowdhury et al.
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Author
Tajwaar Shafiq
ML Research Contributor for Qatar Computing Research Institute (QCRI)
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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.
Tajwaar Shafiq, Hunzalah Hassan Bhatti, S. Chowdhury et al.
· 0 citations