  In this work, we introduce several schemes to leverage description-augmented
embedding similarity for dataless intent classification using current
state-of-the-art (SOTA) text embedding models. We report results of our methods
on four commonly used intent classification datasets and compare against
previous works of a similar nature. Our work shows promising results for
dataless classification scaling to a large number of unseen intents. We show
competitive results and significant improvements (+6.12\% Avg.) over strong
zero-shot baselines, all without training on labelled or task-specific data.
Furthermore, we provide qualitative error analysis of the shortfalls of this
methodology to help guide future research in this area.
