  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.
