Exploring Description-Augmented Dataless Intent Classification
Ruoyu Hu, Foaad Khosmood, Abbas Edalat · 2024
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.