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arxiv: 2604.06784 · v2 · submitted 2026-04-08 · 💻 cs.CL

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

Zhiyu Cao , Peifeng Li , Qiaoming Zhu This is my paper

Pith reviewed 2026-05-10 18:00 UTC · model grok-4.3

classification 💻 cs.CL
keywords multi-party dialogue generationcontext rewritingdiscourse coherenceresponse qualitypreference dataself-evolution learningdialogue systems
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The pith

Rewriting multi-party dialogue contexts with discourse coherence and response quality feedback improves generated responses via iterative self-evolution.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper sets out to show that multi-party dialogue generation suffers from messy, incomplete utterances that obscure structure, and that a dedicated rewriting step can fix this before response generation. The core idea uses two automatic signals, one for how coherent the full dialogue flows and one for how good the final response turns out, to build preference data that trains both a context rewriter and a responder. These components then improve each other in a closed loop of iterative training without needing extra human labels. A reader should care because real conversations are full of slang and interruptions that standard models handle poorly, and this method offers a way to clean the input automatically. Experiments on four separate multi-party datasets are presented as evidence that the rewritten contexts lead to stronger responses.

Core claim

The DRCR framework improves multi-party dialogue generation by rewriting dialogue context using preference data constructed from discourse coherence and response quality signals, then applying a dynamic self-evolution learning method that lets the rewriter and responder mutually refine their outputs through repeated interaction cycles.

What carries the argument

The DRCR framework, which constructs preference pairs from automatic discourse coherence scores and response quality evaluations to train a context rewriter before the responder generates replies, all inside an iterative self-evolution loop.

If this is right

  • The rewriter learns to remove or clarify incomplete and colloquial utterances so the responder receives cleaner input.
  • Mutual improvement occurs because better contexts produce higher-quality responses that in turn provide stronger training signals back to the rewriter.
  • The approach works across four different multi-party dialogue datasets without relying on manual structural annotations.
  • Generation fidelity increases because the model addresses the root problem of weakened dialogue structure representations before producing replies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same rewriting loop could be tested on two-party chat data to see whether the coherence signal still adds value when structural noise is lower.
  • Automatic preference construction from these signals might reduce dependence on large human-annotated dialogue corpora in other conversational tasks.
  • Longer multi-turn conversations could be handled by applying the rewriter at each step rather than only at the start of response generation.

Load-bearing premise

The automatically constructed preference data from discourse coherence and response quality signals accurately captures genuine improvements without introducing systematic biases or circular reinforcement in the self-evolution loop.

What would settle it

Training the system on one multi-party dataset and testing on another held-out set where responses from the full DRCR pipeline show no improvement in automatic coherence or quality metrics over a baseline that generates responses directly from the original context.

Figures

Figures reproduced from arXiv: 2604.06784 by Peifeng Li, Qiaoming Zhu, Zhiyu Cao.

Figure 1
Figure 1. Figure 1: An example of multi-party dialogue gener [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of DRCR, which includes three stages: 1) Train Addressee Recognition Classifier: Identifying [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Case study on context rewriting and response [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Analysis of scaling sampled candidate con [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The impact of rewriting threshold ϕ on model performance in Ubuntu IRC-16 (top) and Ubuntu IRC￾19 (bottom). the availability of higher quality preference data af￾ter expanding the exploration space, which in turn facilitates the training of the rewriter and responder. I Analysis of Rewriting Threshold ϕ During the rewriting process, we adaptively rewrite the dialogue context by setting the threshold ϕ. We … view at source ↗
Figure 7
Figure 7. Figure 7: The prompt used for utterance-level rewriting in Section [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The prompt used for response generation in Section [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt used to evaluate the quality of responses using LLM. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: A case where incorrect rewriting affects response generation. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes the DRCR framework for multi-party dialogue generation. It rewrites dialogue context using two feedback signals—discourse coherence and response quality—to construct preference data for training a context rewriter and a responder. A dynamic self-evolution learning method enables iterative mutual improvement between the rewriter and responder. The abstract claims that comprehensive experiments on four multi-party dialogue datasets substantiate the framework's effectiveness in addressing colloquial expressions and incomplete utterances.

Significance. If the results hold after addressing the concerns below, the work could offer a useful direction for handling noisy, incomplete multi-party dialogues via iterative rewriting guided by automatic signals. The idea of complementary feedback for self-evolution is conceptually appealing for generation tasks, though its practical impact hinges on whether the signals provide genuine, non-circular improvements.

major comments (2)
  1. [Abstract and dynamic self-evolution learning method] The dynamic self-evolution loop (described in the abstract and method overview) constructs preference pairs using discourse coherence and response quality signals derived from the evolving models' own outputs. This setup risks circular reinforcement, where early model artifacts or dataset noise (e.g., colloquial/incomplete utterances) are labeled as preferred and amplified across iterations, rather than identifying genuine improvements. The manuscript must demonstrate that the signals are independently validated and do not share parameters, training data, or initialization with the rewriter/responder.
  2. [Experiments section (referenced in abstract)] The central claim of effectiveness on four datasets is unsupported in the provided text. No implementation details, baselines, evaluation metrics, ablation studies, or quantitative results (e.g., tables comparing DRCR to prior methods) are supplied, leaving the headline assertion unverifiable and the experimental section load-bearing for the paper's contribution.
minor comments (2)
  1. Define the DRCR acronym at first use and clarify the exact architecture of the rewriter and responder (e.g., base models, training objectives).
  2. Add a dedicated limitations or failure-mode discussion addressing potential biases in the automatic preference construction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our DRCR framework. The comments highlight important aspects of the self-evolution mechanism and experimental presentation, which we address point by point below. We believe these clarifications will strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and dynamic self-evolution learning method] The dynamic self-evolution loop (described in the abstract and method overview) constructs preference pairs using discourse coherence and response quality signals derived from the evolving models' own outputs. This setup risks circular reinforcement, where early model artifacts or dataset noise (e.g., colloquial/incomplete utterances) are labeled as preferred and amplified across iterations, rather than identifying genuine improvements. The manuscript must demonstrate that the signals are independently validated and do not share parameters, training data, or initialization with the rewriter/responder.

    Authors: We acknowledge the valid concern about potential circular reinforcement in iterative self-evolution setups. In DRCR, the discourse coherence signal is computed via a fixed, pre-trained coherence scorer (based on an independent model with separate parameters and no shared training data or initialization with the rewriter/responder). The response quality signal uses the responder's outputs to construct preferences but is decoupled through a separate reward model and validated via human preference annotations collected independently. We will revise the method section to explicitly detail these separations, add an ablation isolating each signal, and include iteration-wise performance curves to show non-circular gains. This addresses the risk without altering the core approach. revision: partial

  2. Referee: [Experiments section (referenced in abstract)] The central claim of effectiveness on four datasets is unsupported in the provided text. No implementation details, baselines, evaluation metrics, ablation studies, or quantitative results (e.g., tables comparing DRCR to prior methods) are supplied, leaving the headline assertion unverifiable and the experimental section load-bearing for the paper's contribution.

    Authors: We apologize for any lack of clarity in the initial version; the full manuscript includes a dedicated Experiments section (Section 4) with all requested elements: implementation details (model sizes, training hyperparameters, and optimization), baselines (including prior multi-party methods and context-rewriting approaches), metrics (automatic: BLEU, ROUGE, Distinct-2; human: coherence, relevance, fluency), ablation studies on each feedback signal, and quantitative tables demonstrating consistent improvements over baselines across the four datasets. We will expand this section with additional implementation specifics and ensure all tables are cross-referenced in the abstract and introduction for verifiability. revision: yes

Circularity Check

0 steps flagged

No circularity: framework uses independent feedback signals and reports empirical validation on external datasets

full rationale

The paper introduces DRCR as a framework that constructs preference data from discourse coherence and response quality signals, then applies a dynamic self-evolution loop for iterative training of rewriter and responder. No equations, definitions, or self-citations are presented that reduce the claimed performance gains to the input signals by construction. The signals are described as complementary external feedback rather than outputs of the same evolving models, and effectiveness is asserted via experiments on four separate multi-party dialogue datasets. This leaves the central claim self-contained against external benchmarks rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review provides no explicit free parameters, background axioms, or independently evidenced invented entities beyond the high-level framework description itself.

invented entities (1)
  • DRCR framework no independent evidence
    purpose: To improve multi-party dialogue generation via context rewriting guided by coherence and response signals
    Newly proposed integrated system whose effectiveness rests on unverified experimental claims in the abstract.

pith-pipeline@v0.9.0 · 5441 in / 1089 out tokens · 31414 ms · 2026-05-10T18:00:43.354713+00:00 · methodology

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Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages

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    InProceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, pages 7681–7692

    MADNet: Maximizing addressee deduction expectation for multi-party conversation generation. InProceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, pages 7681–7692. Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao, Zhen- Hua Ling, Huang Hu, Xiubo Geng, and Daxin Jiang

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    DRCR (MPC)

    Direct preference optimization: Your language model is secretly a reward model. InAdvances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Sys- tems 2023, NeurIPS 2023. Boaz Shmueli and Lun-Wei Ku. 2019. Socialnlp emo- tionx 2019 challenge overview: Predicting emo- tions in spoken dialogues and chats.arXiv p...

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    Interleaved thread disambiguation. When multiple conversation threads are active simultane- ously, even strong LLMs can conflate information across threads, producing responses that incorrectly merge topics or address the wrong conversational thread. This is because the flat sequential presen- tation of multi-party dialogue obscures the under- lying reply...

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    Multi- party dialogues frequently contain implicit ad- dressees and pronouns whose antecedents span across utterances from different speakers

    Addressee and reference resolution. Multi- party dialogues frequently contain implicit ad- dressees and pronouns whose antecedents span across utterances from different speakers. While strong LLMs handle coreference well in well struc- tured text, the fragmented and colloquial nature of multi-party conversation degrades their perfor- mance noticeably

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    DRCR (Acc)

    Information scattering. Relevant context for generating an appropriate response may be dis- MethodB 1 B2 B3 B4 MR R L GPT-4.1 (zero-shot) 15.37 7.16 4.35 2.91 7.13 13.49 GPT-4.1 (1-shot) 15.54 7.42 4.55 3.06 7.35 13.72 GPT-4.1 (4-shot) 15.46 7.28 4.31 2.97 7.22 13.56 Qwen3-32B (zero-shot) 15.68 7.37 4.56 3.24 7.46 13.61 Qwen3-32B (1-shot) 15.77 7.51 4.69 ...

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    hardware-driver tool

    is only 13.07, which is much worse than the performance ofQwen3-4Bas a teacher model. The performance fluctuations due to different LLMs on the data construction of the warm-up phase are minor compared to the enhancements brought about by mutual self-evolution (e.g., the B1 on IRC-19 improved by 1.94 in Table 2). This is due to the fact that the warm-up p...

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    Speaker 1: what is the best desktop search for ubuntu ? i just found beagle

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    Speaker 2: best is subjective , but tracker was included by default in gutsy , so i suppose you could say that ubuntu developers think tracker is the best

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    Speaker 3: so stop the whining and use masm

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    Speaker 4: find -name ’keyword ’ for the win

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    Speaker 2: tracker ? it ’s a desktop search applications

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    Speaker 2: then how , pray tell , will i be using it ?

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    Speaker 2: Response: is there some magical linux port of masm that i have n’t heard of ? Example 2: Conversation:

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    Speaker 1: how can i conveniently open an iso ? like without mounting it from cli

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    Speaker 2: open it with archive manager

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    Speaker 1: does that work ? what archive manager= ?

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    file-roller

    Speaker 3: you can open it with the archive manager ( its name is “ file-roller ” , the app that opens FILEPATH files ) then it ’ll be mounted as an archive , but you ’ll have to extract the data from it . it ’s not as convenient as mounting

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    Speaker 3: thus , install gmountiso if you want to graphically manage your mounting points EMOJI

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    You only need to generate the response, do not output any extra content

    Speaker 2: Response: i just opened an iso with the built in archive manager Please generate the final response based on the context and structure of the conversation. You only need to generate the response, do not output any extra content. Conversation: {Conversation_context} Response: Figure 8: The prompt used for response generation in Section 3.3. Prom...

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