# Personalization in Goal-Oriented Dialog

arXiv: https://arxiv.org/abs/1706.07503 · HTML: https://mlsanity.com/paper/1706.07503/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2017.

## Built on by (most cited first, 29 of 29)

- [PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants](https://mlsanity.com/paper/2506.09902/) (2025)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge](https://mlsanity.com/paper/2112.08619/) (2021)
- [A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles](https://mlsanity.com/paper/2102.08322/) (2021)
- [Generating Personalized Dialogue via Multi-Task Meta-Learning](https://mlsanity.com/paper/2108.03377/) (2021)
- [Extracting and Inferring Personal Attributes from Dialogue](https://mlsanity.com/paper/2109.12702/) (2021)
- [Computational Politeness in Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.12814/) (2024)
- [Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function](https://mlsanity.com/paper/2303.13797/) (2023)
- [PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue](https://mlsanity.com/paper/2302.06674/) (2023)
- [Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?](https://mlsanity.com/paper/2305.15908/) (2023)
- [Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks](https://mlsanity.com/paper/2105.10323/) (2021)
- [EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments](https://mlsanity.com/paper/2111.00310/) (2021)
- [Interpreting User Requests in the Context of Natural Language Standing Instructions](https://mlsanity.com/paper/2311.09796/) (2023)
- [A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots](https://mlsanity.com/paper/2401.00609/) (2023)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Predicting gender and age categories in English conversations using lexical, non-lexical, and turn-taking features](https://mlsanity.com/paper/2102.13355/) (2021)
- [Intent Recognition in Conversational Recommender Systems](https://mlsanity.com/paper/2212.03721/) (2022)
- [Tailoring Vaccine Messaging with Common-Ground Opinions](https://mlsanity.com/paper/2405.10861/) (2024)
- [PicPersona-TOD : A Dataset for Personalizing Utterance Style in Task-Oriented Dialogue with Image Persona](https://mlsanity.com/paper/2504.17390/) (2025)
- [Persona-Knowledge Dialogue Multi-Context Retrieval and Enhanced Decoding Methods](https://mlsanity.com/paper/2207.13919/) (2022)
- [Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues](https://mlsanity.com/paper/2401.12995/) (2024)
- [Memory-Augmented Generative Adversarial Transformers](https://mlsanity.com/paper/2402.19218/) (2024)
- [A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health](https://mlsanity.com/paper/2411.02594/) (2024)
- [Is Passive Expertise-Based Personalization Enough? A Case Study in AI-Assisted Test-Taking](https://mlsanity.com/paper/2511.23376/) (2025)
- [Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks](https://mlsanity.com/paper/2110.15724/) (2021)
- [Recent Progress in Conversational AI](https://mlsanity.com/paper/2204.09719/) (2022)
- [Speaker Profiling in Multiparty Conversations](https://mlsanity.com/paper/2304.08801/) (2023)
- [PARAN: Persona-Augmented Review ANswering system on Food Delivery Review Dataset](https://mlsanity.com/paper/2512.10148/) (2025)
- [ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution](https://mlsanity.com/paper/2610.02873/) (2026)

## Abstract (arXiv)

The main goal of modeling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios. End-to-end trained neural dialog systems are an important line of research for such generalized dialog models as they do not resort to any situation-specific handcrafting of rules. However, incorporating personalization into such systems is a largely unexplored topic as there are no existing corpora to facilitate such work. In this paper, we present a new dataset of goal-oriented dialogs which are influenced by speaker profiles attached to them. We analyze the shortcomings of an existing end-to-end dialog system based on Memory Networks and propose modifications to the architecture which enable personalization. We also investigate personalization in dialog as a multi-task learning problem, and show that a single model which shares features among various profiles outperforms separate models for each profile.