# Bilateral Personalized Dialogue Generation with Contrastive Learning

arXiv: https://arxiv.org/abs/2106.07857 · HTML: https://mlsanity.com/paper/2106.07857/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 55 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://mlsanity.com/paper/2106.01890/) (2021)
- [Personalizing Dialogue Agents via Meta-Learning](https://mlsanity.com/paper/1905.10033/) (2019)
- [A Large-Scale Chinese Short-Text Conversation Dataset](https://mlsanity.com/paper/2008.03946/) (2020)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data](https://mlsanity.com/paper/1911.04700/) (2019)
- [Exploiting Persona Information for Diverse Generation of Conversational Responses](https://mlsanity.com/paper/1905.12188/) (2019)
- [Contrastive Learning for Image Captioning](https://mlsanity.com/paper/1710.02534/) (2017)
- [Transfer Learning for Speech and Language Processing](https://mlsanity.com/paper/1511.06066/) (2015)
- [Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions](https://mlsanity.com/paper/2006.12442/) (2020)
- [Neural Personalized Response Generation as Domain Adaptation](https://mlsanity.com/paper/1701.02073/) (2017)
- [Personalizing a Dialogue System with Transfer Reinforcement Learning](https://mlsanity.com/paper/1610.02891/) (2016)
- [Group-wise Contrastive Learning for Neural Dialogue Generation](https://mlsanity.com/paper/2009.07543/) (2020)
- [A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation](https://mlsanity.com/paper/2002.02153/) (2020)
- [Pragmatic Neural Language Modelling in Machine Translation](https://mlsanity.com/paper/1412.7119/) (2014)
- [Multi-Turn Beam Search for Neural Dialogue Modeling](https://mlsanity.com/paper/1906.00141/) (2019)

## Built on by (most cited first, 8 of 8)

- [SCORE: Story Coherence and Retrieval Enhancement for AI Narratives](https://mlsanity.com/paper/2503.23512/) (2025)
- [Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages](https://mlsanity.com/paper/2204.04344/) (2022)
- [Music Audio-Visual Question Answering Requires Specialized Multimodal Designs](https://mlsanity.com/paper/2505.20638/) (2025)
- [Learning Sparsity for Effective and Efficient Music Performance Question Answering](https://mlsanity.com/paper/2506.01319/) (2025)
- [Free-Mask: A Novel Paradigm of Integration Between the Segmentation Diffusion Model and Image Editing](https://mlsanity.com/paper/2411.01819/) (2024)
- [Efficient Temporal Consistency in Diffusion-Based Video Editing with Adaptor Modules: A Theoretical Framework](https://mlsanity.com/paper/2504.16016/) (2025)
- [TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models](https://mlsanity.com/paper/2505.23312/) (2025)
- [TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generation](https://mlsanity.com/paper/2503.17669/) (2025)

## Abstract (arXiv)

Generating personalized responses is one of the major challenges in natural human-robot interaction. Current researches in this field mainly focus on generating responses consistent with the robot's pre-assigned persona, while ignoring the user's persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a Bilateral Personalized Dialogue Generation (BPDG) method for dyadic conversation, which integrates user and robot personas into dialogue generation via designing a dynamic persona-aware fusion method. To bridge the gap between the learning objective function and evaluation metrics, the Conditional Mutual Information Maximum (CMIM) criterion is adopted with contrastive learning to select the proper response from the generated candidates. Moreover, a bilateral persona accuracy metric is designed to measure the degree of bilateral personalization. Experimental results demonstrate that, compared with several state-of-the-art methods, the final results of the proposed method are more personalized and consistent with bilateral personas in terms of both automatic and manual evaluations.