Unraveling Intricacies: A Decomposition Approach for Few-Shot Multi-Intent Spoken Language Understanding

Few-shot multi-intent spoken language understanding (SLU) aims to detect user’s multiple intents and key slots using a tiny amount of annotated data. Prevailing multi-intent SLU models typically rely on abundant data for effective training, enabling them to capture corresponding relationships between intents and slots. However, in few-shot scenarios, establishing these connections becomes challenging, especially in situations involving multiple intents, which may result in confused relationships between intents and slots. To overcome the challenge, we propose decomposing the multi-label intent detection task into several single-label tasks, which reduces the complexity of model training while preserving the constraint relationships between intents and their related slots. We design description templates for each intent and respectively predict the correlation between the utterance and each intent description while completing the slot filling task under the corresponding intent. Therefore, each intent can independently guide the slot filling process, mitigating potentially confused relationships between multiple intents and slots. Experimental results on public datasets indicate that the performance of our model is better than ChatGPT and achieves state-of-the-art results.