  Task oriented Dialogue Systems generally employ intent detection systems in
order to map user queries to a set of pre-defined intents. However, user
queries appearing in natural language can be easily ambiguous and hence such a
direct mapping might not be straightforward harming intent detection and
eventually the overall performance of a dialogue system. Moreover, acquiring
domain-specific clarification questions is costly. In order to disambiguate
queries which are ambiguous between two intents, we propose a novel method of
generating discriminative questions using a simple rule based system which can
take advantage of any question generation system without requiring annotated
data of clarification questions. Our approach aims at discrimination between
two intents but can be easily extended to clarification over multiple intents.
Seeking clarification from the user to classify user intents not only helps
understand the user intent effectively, but also reduces the roboticity of the
conversation and makes the interaction considerably natural.
