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        <title>Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition</title>
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        <namePart type="given">Floris</namePart>
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    <name type="personal">
        <namePart type="given">Arda</namePart>
        <namePart type="family">Kaygan</namePart>
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            <namePart type="given">Helena</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-naacl.156</identifier>
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%0 Conference Proceedings
%T Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition
%A Hengst, Floris
%A Wolter, Ralf
%A Altmeyer, Patrick
%A Kaygan, Arda
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F hengst-etal-2024-conformal
%X 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.
%R 10.18653/v1/2024.findings-naacl.156
%U https://aclanthology.org/2024.findings-naacl.156
%U https://doi.org/10.18653/v1/2024.findings-naacl.156
%P 2412-2432

Markdown (Informal)

[Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition](https://aclanthology.org/2024.findings-naacl.156) (Hengst et al., Findings 2024)

• Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition (Hengst et al., Findings 2024)

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.