  Intent classifiers must be able to distinguish when a user's utterance does
not belong to any supported intent to avoid producing incorrect and unrelated
system responses. Although out-of-scope (OOS) detection for intent classifiers
has been studied, previous work has not yet studied changes in classifier
performance against hard-negative out-of-scope utterances (i.e., inputs that
share common features with in-scope data, but are actually out-of-scope). We
present an automated technique to generate hard-negative OOS data using
ChatGPT. We use our technique to build five new hard-negative OOS datasets, and
evaluate each against three benchmark intent classifiers. We show that
classifiers struggle to correctly identify hard-negative OOS utterances more
than general OOS utterances. Finally, we show that incorporating hard-negative
OOS data for training improves model robustness when detecting hard-negative
OOS data and general OOS data. Our technique, datasets, and evaluation address
an important void in the field, offering a straightforward and inexpensive way
to collect hard-negative OOS data and improve intent classifiers' robustness.
