# Neural Personalized Response Generation as Domain Adaptation

arXiv: https://arxiv.org/abs/1701.02073 · HTML: https://mlsanity.com/paper/1701.02073/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2017.

## Built on by (most cited first, 19 of 19)

- [Long Time No See! Open-Domain Conversation with Long-Term Persona Memory](https://mlsanity.com/paper/2203.05797/) (2022)
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- [The Oscars of AI Theater: A Survey on Role-Playing with Language Models](https://mlsanity.com/paper/2407.11484/) (2024)
- [On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.03154/) (2022)
- [Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters](https://mlsanity.com/paper/2211.06869/) (2022)
- [Transferable Persona-Grounded Dialogues via Grounded Minimal Edits](https://mlsanity.com/paper/2109.07713/) (2021)
- [A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles](https://mlsanity.com/paper/2102.08322/) (2021)
- [Bilateral Personalized Dialogue Generation with Contrastive Learning](https://mlsanity.com/paper/2106.07857/) (2021)
- [Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function](https://mlsanity.com/paper/2303.13797/) (2023)
- [COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation](https://mlsanity.com/paper/2210.15500/) (2022)
- [A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots](https://mlsanity.com/paper/2401.00609/) (2023)
- [Attention-based Contextual Language Model Adaptation for Speech Recognition](https://mlsanity.com/paper/2106.01451/) (2021)
- [Hierarchical Inductive Transfer for Continual Dialogue Learning](https://mlsanity.com/paper/2203.10484/) (2022)
- [Stylistic Retrieval-based Dialogue System with Unparallel Training Data](https://mlsanity.com/paper/2109.05477/) (2021)
- [User Persona Identification and New Service Adaptation Recommendation](https://mlsanity.com/paper/2311.10773/) (2023)
- [A Conditional Generative Chatbot using Transformer Model](https://mlsanity.com/paper/2306.02074/) (2023)
- [A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generation](https://mlsanity.com/paper/2410.20174/) (2024)
- [A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation](https://mlsanity.com/paper/2410.20766/) (2024)
- [GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems](https://mlsanity.com/paper/2606.21097/) (2026)

## Abstract (arXiv)

In this paper, we focus on the personalized response generation for conversational systems. Based on the sequence to sequence learning, especially the encoder-decoder framework, we propose a two-phase approach, namely initialization then adaptation, to model the responding style of human and then generate personalized responses. For evaluation, we propose a novel human aided method to evaluate the performance of the personalized response generation models by online real-time conversation and offline human judgement. Moreover, the lexical divergence of the responses generated by the 5 personalized models indicates that the proposed two-phase approach achieves good results on modeling the responding style of human and generating personalized responses for the conversational systems.