Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems

Understand

Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline.

• However, the underlying supervised models require a large number of labeled pairs, and these pairs are hard and costly to be collected.
• Therefore, We propose an augmentation framework that learns patterns from existing training pairs and generates rewrite candidates from rewrite labels inversely to compensate for insufficient QR training data.
• The proposed framework casts the augmentation problem as a sequence-to-sequence generation task and enforces the optimization process with a policy gradient technique for controllable rewarding.