Conformal Intent Classification and Clarification for Fast and Accurate
  Intent Recognition
Floris den Hengst Ralf Wolter Patrick Altmeyer Arda Kaygan
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.