The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users' true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the existing ACQs studies is their incomparability, from inconsistent use of data, distinct experimental setups and evaluation strategies. Therefore, in this paper, to assist the development of ACQs techniques, we comprehensively analyse the current ACQs research status, which offers a detailed comparison of publicly available datasets, and discusses the applied evaluation metrics, joined with benchmarks for multiple ACQs-related tasks. In particular, given a thorough analysis of the ACQs task, we discuss a number of corresponding research directions for the investigation of ACQs as well as the development of conversational systems.

翻译：理解用户的潜在需求对于对话系统至关重要，尤其是在对话中用户输入信息有限的情况下。因此，在此领域，通过提出澄清问题（Asking Clarification Questions, ACQs）从用户查询或话语中揭示其真实意图成为一项关键任务。然而，现有ACQs研究的一个显著局限性在于其不可比性，这源于数据使用的不一致性、实验设置和评估策略的差异性。为此，本文旨在推动ACQs技术的发展，全面分析了当前ACQs研究现状，对公开可用数据集进行了详细比较，并讨论了应用评估指标及多项ACQs相关任务的基准。特别是，基于对ACQs任务的深入分析，我们探讨了ACQs研究及对话系统发展的若干相应研究方向。