Aslema at NADI 2026: Data Augmentation for Intent Recognition and Slot Filling
Quick summary
arXiv:2608.18689v2 Announce Type: replace-cross Abstract: We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling. We evaluate four omni LLMs in a zero-shot setting and compare them with fine-tuned models. Our results show that fine-tuning consistently outperforms zero-shot inference. We further explore synthetic data augmentation by using an LLM to generate culturally grounded Tunisian Derja utterances, followed by voice cloning to generate synthetic speech. Incorporating this synthetic data improves performance on both ta
Key takeaways
- arXiv:2608.18689v2 Announce Type: replace-cross Abstract: We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling.
- We evaluate four omni LLMs in a zero-shot setting and compare them with fine-tuned models.
- Our results show that fine-tuning consistently outperforms zero-shot inference.
Why it matters
The importance of “Aslema at NADI 2026: Data Augmentation for Intent Recognition and Slot Filling” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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