There is increasing interest in developing personalized Task-oriented
Dialogue Systems (TDSs). Previous work on personalized TDSs often assumes that
complete user profiles are available for most or even all users. This is
unrealistic because (1) not everyone is willing to expose their profiles due to
privacy concerns; and (2) rich user profiles may involve a large number of
attributes (e.g., gender, age, tastes, . . .). In this paper, we study
personalized TDSs without assuming that user profiles are complete. We propose
a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually
enrich user profiles as dialogues progress and to simultaneously improve
response selection based on the enriched profiles. CoMemNN consists of two core
modules: User Profile Enrichment (UPE) and Dialogue Response Selection (DRS).
The former enriches incomplete user profiles by utilizing collaborative
information from neighbor users as well as current dialogues. The latter uses
the enriched profiles to update the current user query so as to encode more
useful information, based on which a personalized response to a user request is
selected.
We conduct extensive experiments on the personalized bAbI dialogue benchmark
datasets. We find that CoMemNN is able to enrich user profiles effectively,
which results in an improvement of 3.06% in terms of response selection
accuracy compared to state-of-the-art methods. We also test the robustness of
CoMemNN against incompleteness of user profiles by randomly discarding
attribute values from user profiles. Even when discarding 50% of the attribute
values, CoMemNN is able to match the performance of the best performing
baseline without discarding user profiles, showing the robustness of CoMemNN.

本文提出了一种无需完整用户信息的个性化任务型对话系统，采用 Cooperative Memory Network 模型逐步丰富用户配置文件，并以此为基础选择个性化回应，实验表明该模型在提高回应准确率的同时具有较强的稳健性。