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 https://github.com/bozheng-hit/MMNLU-22-HIT-SCIR.} ranked
1st in the MMNLU-22 competition under the full-dataset setting.

本文提出使用基于混合数据增强策略的一致性正则化方法来提高多语言口语理解任务中意图检测和 slot 填充两个子任务的性能，实验结果表明该方法在 MASSIVE 数据集上的表现有明显的提升，在 MMNLU-22 竞赛的全数据集设置中排名第一。