  A long-standing challenge for search and conversational assistants is query
intention detection in ambiguous queries. Asking clarifying questions in
conversational search has been widely studied and considered an effective
solution to resolve query ambiguity. Existing work have explored various
approaches for clarifying question ranking and generation. However, due to the
lack of real conversational search data, they have to use artificial datasets
for training, which limits their generalizability to real-world search
scenarios. As a result, the industry has shown reluctance to implement them in
reality, further suspending the availability of real conversational search
interaction data. The above dilemma can be formulated as a cold start problem
of clarifying question generation and conversational search in general.
Furthermore, even if we do have large-scale conversational logs, it is not
realistic to gather training data that can comprehensively cover all possible
queries and topics in open-domain search scenarios. The risk of fitting bias
when training a clarifying question retrieval/generation model on
incomprehensive dataset is thus another important challenge.
  In this work, we innovatively explore generating clarifying questions in a
zero-shot setting to overcome the cold start problem and we propose a
constrained clarifying question generation system which uses both question
templates and query facets to guide the effective and precise question
generation. The experiment results show that our method outperforms existing
state-of-the-art zero-shot baselines by a large margin. Human annotations to
our model outputs also indicate our method generates 25.2\% more natural
questions, 18.1\% more useful questions, 6.1\% less unnatural and 4\% less
useless questions.
