  Acquiring training data to improve the robustness of dialog systems can be a
painstakingly long process. In this work, we propose a method to reduce the
cost and effort of creating new conversational agents by artificially
generating more data from existing examples, using paraphrase generation. Our
proposed approach can kick-start a dialog system with little human effort, and
brings its performance to a level satisfactory enough for allowing actual
interactions with real end-users. We experimented with two neural paraphrasing
approaches, namely Neural Machine Translation and a Transformer-based seq2seq
model. We present the results obtained with two datasets in English and in
French:~a crowd-sourced public intent classification dataset and our own
corporate dialog system dataset. We show that our proposed approach increased
the generalization capabilities of the intent classification model on both
datasets, reducing the effort required to initialize a new dialog system and
helping to deploy this technology at scale within an organization.
