Aslema at NADI 2026: Augmentation through Fewshot for SLU

2026-08-19

AI Summary

研究通过微调和数据增强方法提高意图识别和槽填充任务性能，使用Qwen3-Omni-30B模型结合真实与合成数据，提升系统表现。

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 tasks. Our final submitted system, based on Qwen3-Omni-30B and trained with a mixture of original and synthetic data, achieves 86.8% intent accuracy and 34.7 WER on the devtest split. On the official test set it ranks 1st in slot filling (59.5 CoER) and 4th among 8 teams in intent recognition (66.1% accuracy). We release our experimental scripts and will soon share the synthetic dataset to support further research in this area.

Problem

• Intent Recognition
• Slot Filling
• Data Augmentation

Innovation

• Fewshot Augmentation
• Culturally Grounded Data
• Voice Cloning
• Omni LLMs