  The training of task-oriented dialogue systems is often confronted with the
lack of annotated data. In contrast to previous work which augments training
data through expensive crowd-sourcing efforts, we propose four different
automatic approaches to data augmentation at both the word and sentence level
for end-to-end task-oriented dialogue and conduct an empirical study on their
impact. Experimental results on the CamRest676 and KVRET datasets demonstrate
that each of the four data augmentation approaches is able to obtain a
significant improvement over a strong baseline in terms of Success F1 score and
that the ensemble of the four approaches achieves the state-of-the-art results
in the two datasets. In-depth analyses further confirm that our methods
adequately increase the diversity of user utterances, which enables the
end-to-end model to learn features robustly.
