  When users initiate search sessions, their queries are often unclear or might
lack of context; this resulting in inefficient document ranking. Multiple
approaches have been proposed by the Information Retrieval community to add
context and retrieve documents aligned with users' intents. While some work
focus on query disambiguation using users' browsing history, a recent line of
work proposes to interact with users by asking clarification questions or/and
proposing clarification panels. However, these approaches count either a
limited number (i.e., 1) of interactions with user or log-based interactions.
In this paper, we propose and evaluate a fully simulated query clarification
framework allowing multi-turn interactions between IR systems and user agents.
