  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. This approach goes
beyond the traditional heuristics or rule-based augmentation methods and is not
constrained to generate predefined patterns of swapping/replacing words. Our
experimental results show its effectiveness compared with a fully trained QR
baseline and demonstrate its potential application in boosting the QR
performance on low-resource domains or locales.
