  Dialog systems must be capable of incorporating new skills via updates over
time in order to reflect new use cases or deployment scenarios. Similarly,
developers of such ML-driven systems need to be able to add new training data
to an already-existing dataset to support these new skills. In intent
classification systems, problems can arise if training data for a new skill's
intent overlaps semantically with an already-existing intent. We call such
cases collisions. This paper introduces the task of intent collision detection
between multiple datasets for the purposes of growing a system's skillset. We
introduce several methods for detecting collisions, and evaluate our methods on
real datasets that exhibit collisions. To highlight the need for intent
collision detection, we show that model performance suffers if new data is
added in such a way that does not arbitrate colliding intents. Finally, we use
collision detection to construct and benchmark a new dataset, Redwood, which is
composed of 451 ntent categories from 13 original intent classification
datasets, making it the largest publicly available intent classification
benchmark.
