  Clarification resolution plays an important role in various information
retrieval tasks such as interactive question answering and conversational
search. In such context, the user often formulates their information needs as
short and ambiguous queries, some popular search interfaces then prompt the
user to confirm her intent (e.g. "Did you mean ... ?") or to rephrase if
needed. When it comes to dialogue systems, having fluid user-bot exchanges is
key to good user experience. In the absence of such clarification mechanism,
one of the following responses is given to the user: 1) A direct answer, which
can potentially be non-relevant if the intent was not clear, 2) a generic
fallback message informing the user that the retrieval tool is incapable of
handling the query. Both scenarios might raise frustration and degrade the user
experience. To this end, we propose a multi-stage clarification mechanism for
prompting clarification and query selection in the context of a question
answering dialogue system. We show that our proposed mechanism improves the
overall user experience and outperforms competitive baselines with two
datasets, namely the public in-scope out-of-scope dataset and a commercial
dataset based on real user logs.
