Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

Understand

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.