  Multilingual spoken language understanding (SLU) consists of two sub-tasks,
namely intent detection and slot filling. To improve the performance of these
two sub-tasks, we propose to use consistency regularization based on a hybrid
data augmentation strategy. The consistency regularization enforces the
predicted distributions for an example and its semantically equivalent
augmentation to be consistent. We conduct experiments on the MASSIVE dataset
under both full-dataset and zero-shot settings. Experimental results
demonstrate that our proposed method improves the performance on both intent
detection and slot filling tasks. Our system\footnote{The code will be
available at \url{https://github.com/bozheng-hit/MMNLU-22-HIT-SCIR}.} ranked
1st in the MMNLU-22 competition under the full-dataset setting.
