Exploring Description-Augmented Dataless Intent Classification
<|reference_start|>Exploring Description-Augmented Dataless Intent Classification: 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.<|reference_end|>
@article{hu2024exploring,
    title={Exploring Description-Augmented Dataless Intent Classification},
    author={Ruoyu Hu, Foaad Khosmood, Abbas Edalat},
    journal={arXiv preprint arXiv:2407.17862},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17862},
    primaryClass={cs.CL}
}