  Spoken language understanding (SLU) is a core task in task-oriented dialogue
systems, which aims at understanding the user's current goal through
constructing semantic frames. SLU usually consists of two subtasks, including
intent detection and slot filling. Although there are some SLU frameworks joint
modeling the two subtasks and achieving high performance, most of them still
overlook the inherent relationships between intents and slots and fail to
achieve mutual guidance between the two subtasks. To solve the problem, we
propose a multi-level multi-grained SLU framework MMCL to apply contrastive
learning at three levels, including utterance level, slot level, and word level
to enable intent and slot to mutually guide each other. For the utterance
level, our framework implements coarse granularity contrastive learning and
fine granularity contrastive learning simultaneously. Besides, we also apply
the self-distillation method to improve the robustness of the model.
Experimental results and further analysis demonstrate that our proposed model
achieves new state-of-the-art results on two public multi-intent SLU datasets,
obtaining a 2.6 overall accuracy improvement on the MixATIS dataset compared to
previous best models.
