# Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

arXiv: https://arxiv.org/abs/2103.09534 · HTML: https://mlsanity.com/paper/2103.09534/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 74 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 30 of 46)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (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)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [A Deep Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/1711.08611/) (2017)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1612.01627/) (2016)
- [Adversarial Learning for Neural Dialogue Generation](https://mlsanity.com/paper/1701.06547/) (2017)
- [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://mlsanity.com/paper/1506.06714/) (2015)
- [End-to-End Neural Ad-hoc Ranking with Kernel Pooling](https://mlsanity.com/paper/1706.06613/) (2017)
- [From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots](https://mlsanity.com/paper/1801.01957/) (2018)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2004.03588/) (2020)
- [Modeling Multi-turn Conversation with Deep Utterance Aggregation](https://mlsanity.com/paper/1806.09102/) (2018)
- [Text Matching as Image Recognition](https://mlsanity.com/paper/1602.06359/) (2016)
- [Learning to Remember Translation History with a Continuous Cache](https://mlsanity.com/paper/1711.09367/) (2017)
- [Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection](https://mlsanity.com/paper/2009.04703/) (2020)

## Built on by (most cited first, 14 of 14)

- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [The Oscars of AI Theater: A Survey on Role-Playing with Language Models](https://mlsanity.com/paper/2407.11484/) (2024)
- [LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming](https://mlsanity.com/paper/2306.08401/) (2023)
- [A Survey on LLM-based Conversational User Simulation](https://mlsanity.com/paper/2604.24977/) (2026)
- [Capture Salient Historical Information: A Fast and Accurate Non-Autoregressive Model for Multi-turn Spoken Language Understanding](https://mlsanity.com/paper/2206.12209/) (2022)
- [EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems](https://mlsanity.com/paper/2503.23078/) (2025)
- [The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims](https://mlsanity.com/paper/2411.14072/) (2024)
- [AudioRole: An Audio Dataset for Character Role-Playing in Large Language Models](https://mlsanity.com/paper/2509.23435/) (2025)
- [HARMONI: Multimodal Personalization of Multi-User Human-Robot Interactions with LLMs](https://mlsanity.com/paper/2601.19839/) (2026)
- [ChildEval: When large language models meet children's personalities](https://mlsanity.com/paper/2605.27805/) (2026)
- [HeroNet: A Hybrid Retrieval-Generation Network for Conversational Bots](https://mlsanity.com/paper/2301.12400/) (2023)
- [A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generation](https://mlsanity.com/paper/2410.20174/) (2024)

## Abstract (arXiv)

Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-place conversation scenarios, whether a response candidate is suitable not only counts on the given dialogue context but also other backgrounds, e.g., wording habits, user-specific dialogue history content. To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN). Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information; 2) we perform hybrid representation learning on context-response utterances and explicitly incorporate a customized attention mechanism to extract vital information from context-response interactions so as to improve the accuracy of matching. We evaluate our model on two large datasets with user identification, i.e., personalized Ubuntu dialogue Corpus (P-Ubuntu) and personalized Weibo dataset (P-Weibo). Experimental results confirm that our method significantly outperforms several strong models by combining personalized attention, wording behaviors, and hybrid representation learning.