  Task-oriented dialog systems need to know when a query falls outside their
range of supported intents, but current text classification corpora only define
label sets that cover every example. We introduce a new dataset that includes
queries that are out-of-scope---i.e., queries that do not fall into any of the
system's supported intents. This poses a new challenge because models cannot
assume that every query at inference time belongs to a system-supported intent
class. Our dataset also covers 150 intent classes over 10 domains, capturing
the breadth that a production task-oriented agent must handle. We evaluate a
range of benchmark classifiers on our dataset along with several different
out-of-scope identification schemes. We find that while the classifiers perform
well on in-scope intent classification, they struggle to identify out-of-scope
queries. Our dataset and evaluation fill an important gap in the field,
offering a way of more rigorously and realistically benchmarking text
classification in task-driven dialog systems.
