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This paper proposes a multi-task learning approach that leverages turn-taking dynamics to enhance intent recognition in multi-party conversations, outperforming existing methods that ignore interaction patterns.
This paper presents Aslema, a system for the NADI 2026 shared task on spoken language understanding, using fine-tuned audio LLMs and synthetic data augmentation to improve intent recognition and slot filling for Tunisian Derja.