HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding
Bo Zheng, Zhouyang Li, Fuxuan Wei, Qiguang Chen, Libo Qin, Wanxiang Che · 2022
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 1 ranked 1st in the MMNLU-22 competition under the full-dataset setting.