# Learning to Predict Persona Information forDialogue Personalization without Explicit Persona Description

arXiv: https://arxiv.org/abs/2111.15093 · HTML: https://mlsanity.com/paper/2111.15093/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 28 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [Dialogue Natural Language Inference](https://mlsanity.com/paper/1811.00671/) (2018)
- [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)
- [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://mlsanity.com/paper/1506.06714/) (2015)
- [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)
- [Exploiting Persona Information for Diverse Generation of Conversational Responses](https://mlsanity.com/paper/1905.12188/) (2019)
- [Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation](https://mlsanity.com/paper/2004.07672/) (2020)
- [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://mlsanity.com/paper/2108.09355/) (2021)
- [Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models](https://mlsanity.com/paper/2002.05058/) (2020)
- [Guiding Variational Response Generator to Exploit Persona](https://mlsanity.com/paper/1911.02390/) (2019)
- [DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks](https://mlsanity.com/paper/1908.10731/) (2019)

## Built on by (most cited first, 12 of 12)

- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations](https://mlsanity.com/paper/2405.17974/) (2024)
- [PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives](https://mlsanity.com/paper/2305.02364/) (2023)
- [PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization](https://mlsanity.com/paper/2506.12915/) (2025)
- [The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends](https://mlsanity.com/paper/2409.14195/) (2024)
- [DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling](https://mlsanity.com/paper/2412.04905/) (2024)
- [Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain](https://mlsanity.com/paper/2401.06742/) (2024)
- [Toxic Subword Pruning for Dialogue Response Generation on Large Language Models](https://mlsanity.com/paper/2410.04155/) (2024)
- [Exploring the Effectiveness of Methods for Persona Extraction](https://mlsanity.com/paper/2407.09181/) (2024)
- [Personality-Aware Reinforcement Learning for Persuasive Dialogue with LLM-Driven Simulation](https://mlsanity.com/paper/2601.06877/) (2026)
- [PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media](https://mlsanity.com/paper/2605.12525/) (2026)
- [Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study](https://mlsanity.com/paper/2605.16011/) (2026)

## Abstract (arXiv)

Personalizing dialogue agents is important for dialogue systems to generate more specific, consistent, and engaging responses. However, most current dialogue personalization approaches rely on explicit persona descriptions during inference, which severely restricts its application. In this paper, we propose a novel approach that learns to predict persona information based on the dialogue history to personalize the dialogue agent without relying on any explicit persona descriptions during inference. Experimental results on the PersonaChat dataset show that the proposed method can improve the consistency of generated responses when conditioning on the predicted profile of the dialogue agent (i.e. "self persona"), and improve the engagingness of the generated responses when conditioning on the predicted persona of the dialogue partner (i.e. "their persona"). We also find that a trained persona prediction model can be successfully transferred to other datasets and help generate more relevant responses.