  Existing conversational search studies mainly focused on asking better
clarifying questions and/or improving search result quality. These works aim at
retrieving better responses according to the search context, and their
performances are evaluated on either single-turn tasks or multi-turn tasks
under naive conversation policy settings. This leaves some questions about
their applicability in real-world multi-turn conversations where realistically,
each and every action needs to be made by the system itself, and search session
efficiency is often an important concern of conversational search systems.
While some recent works have identified the need for improving search
efficiency in conversational search, they mostly require extensive data
annotations and use hand-crafted rewards or heuristics to train systems that
can achieve reasonable performance in a restricted number of turns, which has
limited generalizability in practice.
  In this paper, we propose a reward-free conversation policy imitation
learning framework, which can train a conversation policy without annotated
conversation data or manually designed rewards. The trained conversation policy
can be used to guide the conversational retrieval models to balance
conversational search quality and efficiency. To evaluate the proposed
conversational search system, we propose a new multi-turn-multi-response
conversational evaluation metric named Expected Conversational Reciprocal Rank
(ECRR). ECRR is designed to evaluate entire multi-turn conversational search
sessions towards comprehensively evaluating both search result quality and
search efficiency.
