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Intelligent systems need to be able to recover from mistakes, resolveuncertainty, and adapt to novel concepts not seen during training. Dialoginteraction can enable this by the use of clarifications for correction andresolving uncertainty, and active learning queries to learn new conceptsencountered during operation. Prior work on dialog systems has either focusedon exclusively learning how to perform clarification/ information seeking, orto perform active learning. In this work, we train a hierarchical dialog policyto jointly perform both clarification and active learning in the context of aninteractive language-based image retrieval task motivated by an online shoppingapplication, and demonstrate that jointly learning dialog policies forclarification and active learning is more effective than the use of staticdialog policies for one or both of these functions.

Dialog Policy Learning for Joint Clarification and Active Learning Queries