Exploring Description-Augmented Dataless Intent Classification

Ruoyu Hu, Foaad Khosmood, Abbas Edalat


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.
Anthology ID:
2024.nlp4convai-1.2
Volume:
Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Elnaz Nouri, Abhinav Rastogi, Georgios Spithourakis, Bing Liu, Yun-Nung Chen, Yu Li, Alon Albalak, Hiromi Wakaki, Alexandros Papangelis
Venues:
NLP4ConvAI | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–36
Language:
URL:
https://aclanthology.org/2024.nlp4convai-1.2
DOI:
Bibkey:
Cite (ACL):
Ruoyu Hu, Foaad Khosmood, and Abbas Edalat. 2024. Exploring Description-Augmented Dataless Intent Classification. In Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024), pages 13–36, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Exploring Description-Augmented Dataless Intent Classification (Hu et al., NLP4ConvAI-WS 2024)
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PDF:
https://aclanthology.org/2024.nlp4convai-1.2.pdf