Exploring Description-Augmented Dataless Intent Classification
This work addresses intent classification for NLP applications without labeled data, but it is incremental as it builds on existing embedding methods.
The paper tackled dataless intent classification by leveraging description-augmented embedding similarity with SOTA text embedding models, achieving a 6.12% average improvement over strong zero-shot baselines without training on labeled data.
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.