A Generative Model for Joint Multiple Intent Detection and Slot Filling

• URL: http://arxiv.org/abs/2602.08322v2
• Date: Thu, 12 Feb 2026 05:47:30 GMT
• Title: A Generative Model for Joint Multiple Intent Detection and Slot Filling
• Authors: Liz Li, Wei Zhu,
• Abstract summary: In task-oriented dialogue systems, spoken language understanding (SLU) is a critical component, which consists of two sub-tasks, intent detection and slot filling.<n>Most existing methods focus on the single-intent SLU, where each utterance only has one intent.<n>In this paper, we propose a generative framework to simultaneously address multiple intent detection and slot filling.
• Score: 3.060720241524644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In task-oriented dialogue systems, spoken language understanding (SLU) is a critical component, which consists of two sub-tasks, intent detection and slot filling. Most existing methods focus on the single-intent SLU, where each utterance only has one intent. However, in real-world scenarios users usually express multiple intents in an utterance, which poses a challenge for existing dialogue systems and datasets. In this paper, we propose a generative framework to simultaneously address multiple intent detection and slot filling. In particular, an attention-over-attention decoder is proposed to handle the variable number of intents and the interference between the two sub-tasks by incorporating an inductive bias into the process of multi-task learning. Besides, we construct two new multi-intent SLU datasets based on single-intent utterances by taking advantage of the next sentence prediction (NSP) head of the BERT model. Experimental results demonstrate that our proposed attention-over-attention generative model achieves state-of-the-art performance on two public datasets, MixATIS and MixSNIPS, and our constructed datasets.

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