Data Augmentation for Spoken Language Understanding via Joint Variational Generation

Understand

Data scarcity is one of the main obstacles of domain adaptation in spoken language understanding (SLU) due to the high cost of creating manually tagged SLU datasets.

• Recent works in neural text generative models, particularly latent variable models such as variational autoencoder (VAE), have shown promising results in regards to generating plausible and natural sentences.
• In this paper, we propose a novel generative architecture which leverages the generative power of latent variable models to jointly synthesize fully annotated utterances.
• Our experiments show that existing SLU models trained on the additional synthetic examples achieve performance gains.