@inproceedings{hu-etal-2024-exploring,
    title = "Exploring Description-Augmented Dataless Intent Classification",
    author = "Hu, Ruoyu  and
      Khosmood, Foaad  and
      Edalat, Abbas",
    editor = "Nouri, Elnaz  and
      Rastogi, Abhinav  and
      Spithourakis, Georgios  and
      Liu, Bing  and
      Chen, Yun-Nung  and
      Li, Yu  and
      Albalak, Alon  and
      Wakaki, Hiromi  and
      Papangelis, Alexandros",
    booktitle = "Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.nlp4convai-1.2",
    pages = "13--36",
    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.",
}