# Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots

arXiv: https://arxiv.org/abs/1908.05859 · HTML: https://mlsanity.com/paper/1908.05859/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 23 of 23)

- [Hello Again! LLM-powered Personalized Agent for Long-term Dialogue](https://mlsanity.com/paper/2406.05925/) (2024)
- [MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding](https://mlsanity.com/paper/2106.01541/) (2021)
- [Personalized Dialogue Generation with Persona-Adaptive Attention](https://mlsanity.com/paper/2210.15088/) (2022)
- [COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas](https://mlsanity.com/paper/2205.00872/) (2022)
- [Improving Personality Consistency in Conversation by Persona Extending](https://mlsanity.com/paper/2208.10816/) (2022)
- [Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization](https://mlsanity.com/paper/2305.12782/) (2023)
- [HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations](https://mlsanity.com/paper/2203.08500/) (2022)
- [Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2105.09050/) (2021)
- [Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot](https://mlsanity.com/paper/2108.07935/) (2021)
- [Content Selection Network for Document-grounded Retrieval-based Chatbots](https://mlsanity.com/paper/2101.08426/) (2021)
- [Transferable Persona-Grounded Dialogues via Grounded Minimal Edits](https://mlsanity.com/paper/2109.07713/) (2021)
- [Detecting Speaker Personas from Conversational Texts](https://mlsanity.com/paper/2109.01330/) (2021)
- [P5: Plug-and-Play Persona Prompting for Personalized Response Selection](https://mlsanity.com/paper/2310.06390/) (2023)
- [Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access](https://mlsanity.com/paper/2212.05373/) (2022)
- [PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment](https://mlsanity.com/paper/2212.08992/) (2022)
- [UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval](https://mlsanity.com/paper/2402.16261/) (2024)
- [StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations](https://mlsanity.com/paper/2109.09853/) (2021)
- [Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection](https://mlsanity.com/paper/2208.09601/) (2022)
- [Partner Personas Generation for Diverse Dialogue Generation](https://mlsanity.com/paper/2111.13833/) (2021)
- [Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicability](https://mlsanity.com/paper/2309.06460/) (2023)
- [Multi-turn Response Selection with Commonsense-enhanced Language Models](https://mlsanity.com/paper/2407.18479/) (2024)
- [Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems](https://mlsanity.com/paper/2509.22845/) (2025)
- [One Instruction Does Not Fit All: How Well Do Embeddings Align Personas and Instructions in Low-Resource Indian Languages?](https://mlsanity.com/paper/2601.10205/) (2026)

## Abstract (arXiv)

This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate. Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates. Experimental results on PERSONA-CHAT dataset show that the DIM model outperforms its baseline model, i.e., IMN with persona fusion, by a margin of 14.5% and outperforms the current state-of-the-art model by a margin of 27.7% in terms of top-1 accuracy hits@1.