Abstract
We present Conformal Intent Classification and Clarification (CICC), a framework for fast and accurate intent classification for task-oriented dialogue systems. The framework turns heuristic uncertainty scores of any intent classifier into a clarification question that is guaranteed to contain the true intent at a pre-defined confidence level.By disambiguating between a small number of likely intents, the user query can be resolved quickly and accurately. Additionally, we propose to augment the framework for out-of-scope detection.In a comparative evaluation using seven intent recognition datasets we find that CICC generates small clarification questions and is capable of out-of-scope detection.CICC can help practitioners and researchers substantially in improving the user experience of dialogue agents with specific clarification questions.- Anthology ID:
- 2024.findings-naacl.156
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2412–2432
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.156
- DOI:
- Cite (ACL):
- Floris Hengst, Ralf Wolter, Patrick Altmeyer, and Arda Kaygan. 2024. Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2412–2432, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition (Hengst et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/fix-volume-bibkeys/2024.findings-naacl.156.pdf