Exploring Description-Augmented Dataless Intent Classification

TL;DR

This paper proposes description-augmented embedding similarity methods for dataless intent classification, achieving significant improvements over zero-shot baselines without using labeled data, and demonstrating scalability to many unseen intents.

Contribution

It introduces novel schemes leveraging description-augmented embeddings for dataless intent classification and evaluates their effectiveness across multiple datasets.

Findings

Achieves +6.12% average improvement over zero-shot baselines.

Demonstrates scalability to large numbers of unseen intents.

Provides qualitative error analysis to guide future research.

Abstract

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.