# Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation

arXiv: https://arxiv.org/abs/2604.06784 · HTML: https://mlsanity.com/paper/2604.06784/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding](https://mlsanity.com/paper/2106.01541/) (2021)
- [GSN: A Graph-Structured Network for Multi-Party Dialogues](https://mlsanity.com/paper/1905.13637/) (2019)
- [HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations](https://mlsanity.com/paper/2203.08500/) (2022)
- [SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats](https://mlsanity.com/paper/1909.07734/) (2019)
- [Dialogue Systems for Emotional Support via Value Reinforcement](https://mlsanity.com/paper/2501.17182/) (2025)
- [MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation](https://mlsanity.com/paper/2305.12733/) (2023)
- [OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System](https://mlsanity.com/paper/2312.16864/) (2023)
- [DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations](https://mlsanity.com/paper/2404.00557/) (2024)
- [SS-MPC: A Sequence-Structured Multi-Party Conversation System](https://mlsanity.com/paper/2502.16920/) (2025)

## Abstract (arXiv)

Previous research on multi-party dialogue generation has predominantly leveraged structural information inherent in dialogues to directly inform the generation process. However, the prevalence of colloquial expressions and incomplete utterances in dialogues often impedes comprehension and weakens the fidelity of dialogue structure representations, which is particularly pronounced in multi-party dialogues. In this work, we propose a novel framework DRCR (Discourse coherence and Response-guided Context Rewriting) to improve multi-party dialogue generation through dialogue context rewriting. Specifically, DRCR employs two complementary feedback signals, discourse coherence and response quality, to construct preference data for both context rewriting and response generation. Moreover, we propose a dynamic self-evolution learning method that allows the rewriter and responder to continuously enhance their capabilities through mutual interaction in an iterative training loop. Comprehensive experiments conducted on four multi-party dialogue datasets substantiate the effectiveness of DRCR.