用于快速准确意图识别的保形意图分类与澄清
Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition
AI总结：
本文提出保形意图分类与澄清框架CICC，将意图分类器的不确定性分数转化为保证包含真实意图的澄清问题，并支持分布外检测，在七个数据集上验证了其高效性与准确性。
摘要：
我们提出了保形意图分类与澄清（CICC），这是一个用于任务导向对话系统的快速准确意图分类框架。该框架将任意意图分类器的启发式不确定性分数转化为一个澄清问题，并保证在预定义的置信水平下包含真实意图。通过在少量可能的意图之间进行消歧，用户查询可以被快速且准确地解决。此外，我们建议扩展该框架以支持分布外检测。在使用七个意图识别数据集进行的对比评估中，我们发现CICC生成了较小的澄清问题，并且具备分布外检测能力。CICC能够通过具体的澄清问题，实质性地帮助从业者和研究人员改善对话代理的用户体验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
