# A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles arXiv: https://arxiv.org/abs/2102.08322 ยท HTML: https://mlsanity.com/paper/2102.08322/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 54 references that are in the map. - First posted on arXiv 16 Feb 2021. ## Builds on (most cited first, 30 of 40) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018) - [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016) - [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016) - [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019) - [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018) - [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017) - [Controlling Linguistic Style Aspects in Neural Language Generation](https://mlsanity.com/paper/1707.02633/) (2017) - [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017) - [Training Millions of Personalized Dialogue Agents](https://mlsanity.com/paper/1809.01984/) (2018) - [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016) - [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014) - [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://mlsanity.com/paper/2004.05388/) (2020) - [Personalizing Dialogue Agents via Meta-Learning](https://mlsanity.com/paper/1905.10033/) (2019) - [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019) - [A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data](https://mlsanity.com/paper/1911.04700/) (2019) - [Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/1804.08217/) (2018) - [Exploiting Persona Information for Diverse Generation of Conversational Responses](https://mlsanity.com/paper/1905.12188/) (2019) - [Training Neural Response Selection for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1906.01543/) (2019) - [Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation](https://mlsanity.com/paper/2004.07672/) (2020) - [Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems](https://mlsanity.com/paper/1511.06931/) (2015) - [Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning](https://mlsanity.com/paper/1702.03274/) (2017) - [Personalization in Goal-Oriented Dialog](https://mlsanity.com/paper/1706.07503/) (2017) - [Neural Personalized Response Generation as Domain Adaptation](https://mlsanity.com/paper/1701.02073/) (2017) - [Learning Personalized End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1811.04604/) (2018) - [Getting To Know You: User Attribute Extraction from Dialogues](https://mlsanity.com/paper/1908.04621/) (2019) - [Personalizing a Dialogue System with Transfer Reinforcement Learning](https://mlsanity.com/paper/1610.02891/) (2016) - [Listening between the Lines: Learning Personal Attributes from Conversations](https://mlsanity.com/paper/1904.10887/) (2019) - [ALOHA: Artificial Learning of Human Attributes for Dialogue Agents](https://mlsanity.com/paper/1910.08293/) (2019) ## Built on by (most cited first, 8 of 8) - [In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents](https://mlsanity.com/paper/2503.08026/) (2025) - [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021) - [Extracting and Inferring Personal Attributes from Dialogue](https://mlsanity.com/paper/2109.12702/) (2021) - [Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function](https://mlsanity.com/paper/2303.13797/) (2023) - [Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues](https://mlsanity.com/paper/2305.13690/) (2023) - [Incremental user embedding modeling for personalized text classification](https://mlsanity.com/paper/2202.06369/) (2022) - [Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues](https://mlsanity.com/paper/2307.06703/) (2023) - [Persona-Aware Alignment Framework for Personalized Dialogue Generation](https://mlsanity.com/paper/2511.10215/) (2025) ## Abstract (arXiv) 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.