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.

我们提出了一种名为 Conformal Intent Classification and Clarification (CICC) 的框架，用于快速准确地对任务导向式对话系统进行意图分类。该框架将任何意图分类器的启发式不确定性分数转化为一个包含真实意图的澄清问题，并且以预定义的置信水平提供保证。通过消除少量可能的意图之间的歧义，可以快速且准确地解决用户查询。此外，我们还提出了用于超范围检测的增强框架。通过对七个意图识别数据集进行比较评估，我们发现 CICC 生成的澄清问题较小且具有超范围检测功能。CICC 可在很大程度上帮助从业者和研究人员通过特定的澄清问题改善对话代理的用户体验。

快速准确的意图识别的一致意图分类和澄清

Conformal Intent Classification and Clarification for Fast and Accurate  Intent Recognition

Users often ask dialogue systems ambiguous questions that require
clarification. We show that current language models rarely ask users to clarify
ambiguous questions and instead provide incorrect answers. To address this, we
introduce CLAM: a framework for getting language models to selectively ask for
clarification about ambiguous user questions. In particular, we show that we
can prompt language models to detect whether a given question is ambiguous,
generate an appropriate clarifying question to ask the user, and give a final
answer after receiving clarification. We also show that we can simulate users
by providing language models with privileged information. This lets us
automatically evaluate multi-turn clarification dialogues. Finally, CLAM
significantly improves language models' accuracy on mixed ambiguous and
unambiguous questions relative to SotA.

CLAM 是一个用于解决自然语言生成系统在回答用户含糊问题时存在的正确性问题的框架，可以发现并提示用户澄清问题，自动评估对话质量，并提升语言模型在混合问题上的准确率。

CLAM: 基于生成式语言模型的模糊问题选择性澄清

CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models

Coping with ambiguous questions has been a perennial problem in real-world
dialogue systems. Although clarification by asking questions is a common form
of human interaction, it is hard to define appropriate questions to elicit more
specific intents from a user. In this work, we propose a reinforcement model to
clarify ambiguous questions by suggesting refinements of the original query. We
first formulate a collection partitioning problem to select a set of labels
enabling us to distinguish potential unambiguous intents. We list the chosen
labels as intent phrases to the user for further confirmation. The selected
label along with the original user query then serves as a refined query, for
which a suitable response can more easily be identified. The model is trained
using reinforcement learning with a deep policy network. We evaluate our model
based on real-world user clicks and demonstrate significant improvements across
several different experiments.

本文提出了通过增强学习模型来澄清模糊问题的方法，包括分割问题、选择标签、确认意图和构建适当的响应。模型基于深度政策网络的强化学习模型，通过真实用户点击数据评估并展示了显著的性能提升。

通过强化学习在对话中进行交互式问题澄清

Interactive Question Clarification in Dialogue via Reinforcement  Learning

Intelligent systems need to be able to recover from mistakes, resolve
uncertainty, and adapt to novel concepts not seen during training. Dialog
interaction can enable this by the use of clarifications for correction and
resolving uncertainty, and active learning queries to learn new concepts
encountered during operation. Prior work on dialog systems has either focused
on exclusively learning how to perform clarification/ information seeking, or
to perform active learning. In this work, we train a hierarchical dialog policy
to jointly perform both clarification and active learning in the context of an
interactive language-based image retrieval task motivated by an online shopping
application, and demonstrate that jointly learning dialog policies for
clarification and active learning is more effective than the use of static
dialog policies for one or both of these functions.

本文介绍了一个针对在线购物应用中的任务的交互式基于语言的图像检索任务中训练分层对话策略来共同执行澄清和主动学习的方法，并表明联合学习对于这两个功能的静态对话策略的使用更有效。