  Multi-intent Spoken Language Understanding has great potential for widespread
implementation. Jointly modeling Intent Detection and Slot Filling in it
provides a channel to exploit the correlation between intents and slots.
However, current approaches are apt to formulate these two sub-tasks
differently, which leads to two issues: 1) It hinders models from effective
extraction of shared features. 2) Pretty complicated structures are involved to
enhance expression ability while causing damage to the interpretability of
frameworks. In this work, we describe a Prompt-based Spoken Language
Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into
the same form by offering a common pre-trained Seq2Seq model. In detail, ID and
SF are completed by concisely filling the utterance into task-specific prompt
templates as input, and sharing output formats of key-value pairs sequence.
Furthermore, variable intents are predicted first, then naturally embedded into
prompts to guide slot-value pairs inference from a semantic perspective.
Finally, we are inspired by prevalent multi-task learning to introduce an
auxiliary sub-task, which helps to learn relationships among provided labels.
Experiment results show that our framework outperforms several state-of-the-art
baselines on two public datasets.
