  Identifying user intents from natural language utterances is a crucial step
in conversational systems that has been extensively studied as a supervised
classification problem. However, in practice, new intents emerge after
deploying an intent detection model. Thus, these models should seamlessly adapt
and classify utterances with both seen and unseen intents -- unseen intents
emerge after deployment and they do not have training data. The few existing
models that target this setting rely heavily on the scarcely available training
data and overfit to seen intents data, resulting in a bias to misclassify
utterances with unseen intents into seen ones. We propose RIDE: an intent
detection model that leverages commonsense knowledge in an unsupervised fashion
to overcome the issue of training data scarcity. RIDE computes robust and
generalizable relationship meta-features that capture deep semantic
relationships between utterances and intent labels; these features are computed
by considering how the concepts in an utterance are linked to those in an
intent label via commonsense knowledge. Our extensive experimental analysis on
three widely-used intent detection benchmarks shows that relationship
meta-features significantly increase the accuracy of detecting both seen and
unseen intents and that RIDE outperforms the state-of-the-art model for unseen
intents.
