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People speak at different levels of specificity in different situations.Depending on their knowledge, interlocutors, mood, etc.} A conversational agentshould have this ability and know when to be specific and when to be general.We propose an approach that gives a neural network--based conversational agentthis ability. Our approach involves alternating between \emph{datadistillation} and model training : removing training examples that are closestto the responses most commonly produced by the model trained from the lastround and then retrain the model on the remaining dataset. Dialogue generationmodels trained with different degrees of data distillation manifest differentlevels of specificity.  We then train a reinforcement learning system for selecting among this poolof generation models, to choose the best level of specificity for a giveninput. Compared to the original generative model trained without distillation,the proposed system is capable of generating more interesting andhigher-quality responses, in addition to appropriately adjusting specificitydepending on the context.  Our research constitutes a specific case of a broader approach involvingtraining multiple subsystems from a single dataset distinguished by differencesin a specific property one wishes to model. We show that from such a set ofsubsystems, one can use reinforcement learning to build a system that tailorsits output to different input contexts at test time.

Data Distillation for Controlling Specificity in Dialogue Generation