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There is increasing interest in developing personalized Task-orientedDialogue Systems (TDSs). Previous work on personalized TDSs often assumes thatcomplete user profiles are available for most or even all users. This isunrealistic because (1) not everyone is willing to expose their profiles due toprivacy concerns; and (2) rich user profiles may involve a large number ofattributes (e.g., gender, age, tastes, . . .). In this paper, we studypersonalized TDSs without assuming that user profiles are complete. We proposea Cooperative Memory Network (CoMemNN) that has a novel mechanism to graduallyenrich user profiles as dialogues progress and to simultaneously improveresponse selection based on the enriched profiles. CoMemNN consists of two coremodules: User Profile Enrichment (UPE) and Dialogue Response Selection (DRS).The former enriches incomplete user profiles by utilizing collaborativeinformation from neighbor users as well as current dialogues. The latter usesthe enriched profiles to update the current user query so as to encode moreuseful information, based on which a personalized response to a user request isselected.  We conduct extensive experiments on the personalized bAbI dialogue benchmarkdatasets. We find that CoMemNN is able to enrich user profiles effectively,which results in an improvement of 3.06% in terms of response selectionaccuracy compared to state-of-the-art methods. We also test the robustness ofCoMemNN against incompleteness of user profiles by randomly discardingattribute values from user profiles. Even when discarding 50% of the attributevalues, CoMemNN is able to match the performance of the best performingbaseline without discarding user profiles, showing the robustness of CoMemNN.

A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles