  Users often formulate their search queries with immature language without
well-developed keywords and complete structures. Such queries fail to express
their true information needs and raise ambiguity as fragmental language often
yield various interpretations and aspects. This gives search engines a hard
time processing and understanding the query, and eventually leads to
unsatisfactory retrieval results. An alternative approach to direct answer
while facing an ambiguous query is to proactively ask clarifying questions to
the user. Recent years have seen many works and shared tasks from both NLP and
IR community about identifying the need for asking clarifying question and
methodology to generate them. An often neglected fact by these works is that
although sometimes the need for clarifying questions is correctly recognized,
the clarifying questions these system generate are still off-topic and
dissatisfaction provoking to users and may just cause users to leave the
conversation.
  In this work, we propose a risk-aware conversational search agent model to
balance the risk of answering user's query and asking clarifying questions. The
agent is fully aware that asking clarifying questions can potentially collect
more information from user, but it will compare all the choices it has and
evaluate the risks. Only after then, it will make decision between answering or
asking. To demonstrate that our system is able to retrieve better answers, we
conduct experiments on the MSDialog dataset which contains real-world customer
service conversations from Microsoft products community. We also purpose a
reinforcement learning strategy which allows us to train our model on the
original dataset directly and saves us from any further data annotation
efforts. Our experiment results show that our risk-aware conversational search
agent is able to significantly outperform strong non-risk-aware baselines.
