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A large-scale conversational agent can suffer from understanding userutterances with various ambiguities such as ASR ambiguity, intent ambiguity,and hypothesis ambiguity. When ambiguities are detected, the agent shouldengage in a clarifying dialog to resolve the ambiguities before committing toactions. However, asking clarifying questions for all the ambiguity occurrencescould lead to asking too many questions, essentially hampering the userexperience. To trigger clarifying questions only when necessary for the usersatisfaction, we propose a neural self-attentive model that leverages thehypotheses with ambiguities and contextual signals. We conduct extensiveexperiments on five common ambiguity types using real data from a large-scalecommercial conversational agent and demonstrate significant improvement over aset of baseline approaches.

Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding