  Coping with ambiguous questions has been a perennial problem in real-world
dialogue systems. Although clarification by asking questions is a common form
of human interaction, it is hard to define appropriate questions to elicit more
specific intents from a user. In this work, we propose a reinforcement model to
clarify ambiguous questions by suggesting refinements of the original query. We
first formulate a collection partitioning problem to select a set of labels
enabling us to distinguish potential unambiguous intents. We list the chosen
labels as intent phrases to the user for further confirmation. The selected
label along with the original user query then serves as a refined query, for
which a suitable response can more easily be identified. The model is trained
using reinforcement learning with a deep policy network. We evaluate our model
based on real-world user clicks and demonstrate significant improvements across
several different experiments.
