  Lack of training data presents a grand challenge to scaling out spoken
language understanding (SLU) to low-resource languages. Although various data
augmentation approaches have been proposed to synthesize training data in
low-resource target languages, the augmented data sets are often noisy, and
thus impede the performance of SLU models. In this paper we focus on mitigating
noise in augmented data. We develop a denoising training approach. Multiple
models are trained with data produced by various augmented methods. Those
models provide supervision signals to each other. The experimental results show
that our method outperforms the existing state of the art by 3.05 and 4.24
percentage points on two benchmark datasets, respectively. The code will be
made open sourced on github.
