  We introduce a data augmentation technique based on byte pair encoding and a
BERT-like self-attention model to boost performance on spoken language
understanding tasks. We compare and evaluate this method with a range of
augmentation techniques encompassing generative models such as VAEs and
performance-boosting techniques such as synonym replacement and
back-translation. We show our method performs strongly on domain and intent
classification tasks for a voice assistant and in a user-study focused on
utterance naturalness and semantic similarity.

Computation and Language;Machine Learning