Papers with dialogue quality
CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization (2025.findings-emnlp)
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| Challenge: | Comparative Policy Optimization (CPO) redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise score. |
| Approach: | They propose a method to optimize subjective tasks by shifting from sample-wise to comparative group-wise scoring. |
| Outcome: | The proposed framework shifts from sample-wise scoring to comparative group-wise score . it minimizes contextual bias and enables more robust and fair performance evaluation. |
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (2022.coling-1)
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| Challenge: | Existing evaluation metrics are expensive and easy to conduct but ineffective to reflect dialogue quality. |
| Approach: | They propose a self-supervised fine-grained dialogue evaluation framework which can automatically assign fine-granular scores for arbitrarily dialogue data. |
| Outcome: | The proposed framework is highly consistent with human evaluations and better than the state-of-the-art models. |
Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys (2024.findings-eacl)
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| Challenge: | integrating cultural dimensions with dialogue encoding features can enhance the predictive accuracy and quality of dialogue agents. |
| Approach: | They propose to incorporate cultural dimensions into dialogue encoding features to enhance the predictive accuracy of dialogue agents. |
| Outcome: | The proposed model improves the accuracy and quality of dialogue predictions by incorporating cultural dimensions with dialogue encoding features. |
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness (2020.emnlp-main)
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| Challenge: | Large-scale dialogue datasets contain a non-negligible number of unacceptable utterance pairs . previous studies have identified such flaws and reported that the corpus is noisy . |
| Approach: | They propose a method for scoring the quality of utterance pairs based on their connectivity and relatedness. |
| Outcome: | The proposed method has a good correlation with human judgment of dialogue quality and is applied to training data filtered by the proposed method. |
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation (2023.findings-acl)
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| Challenge: | Crowdsourced dialogue corpora are limited in scale and topic coverage due to the expensive cost of data curation. |
| Approach: | They construct an augmented dataset for the emotional support conversation task using large language models for dialogue augmentation. |
| Outcome: | The proposed approach outperforms baselines of dialogue augmentation and improves the model's generalization ability to open-domain topics. |
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset (2024.naacl-long)
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| Challenge: | Existing multi-modal dialogue datasets that focus on image-based dialogues have low quality and limited diversity of images per dialogue. |
| Approach: | They propose to construct a multi-modal dialogue dataset that guarantees both dialogue quality and image diversity without requiring minimum human effort. |
| Outcome: | The proposed dataset outperforms existing datasets in terms of quality and diversity in human evaluation. |
Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Existing methods to improve persona consistency are centered around supervised learning or online reinforcement learning (RL). Existing approaches to improve consistency are expensive and require additional training. |
| Approach: | They propose an offline supervised learning framework to improve persona consistency of dialogue systems by punishing and rewarding specific utterances. |
| Outcome: | The proposed framework improves both the persona consistency and dialogue quality of a state-of-the-art social chatbot. |
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)
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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji
| Challenge: | a framework for constructing dialogue world models for natural language tasks is currently lacking. |
| Approach: | They propose a framework that can be used to train a dialogue world model. |
| Outcome: | The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification . |
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on evaluating single-round interactions, neglecting other critical aspects. |
| Approach: | They propose a benchmark to evaluate full-duplex speech language models in multi-round settings . they segment continuous full-dual dialogues into discrete turns for evaluation . |
| Outcome: | The proposed benchmark compared full-duplex speech language models with full-dual speech models . the results show that the models perform better in multi-round settings than standard models compared to benchmarks . |
From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations (2025.emnlp-main)
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| Challenge: | Experimental results show that LLMs can infer persona traits and subtle shifts in emotionality and extraversion occur . scalable solutions with reduced costs and enhanced data privacy are needed . |
| Approach: | They explore the role of personas in the creation of emotional support conversations by LLMs. |
| Outcome: | The proposed model can infer persona traits and maintain key persona characteristics while revealing shifts in emotionality and extraversion. |
Empathy in Diversity: Personalized Depression and Anxiety Therapy via Dialogue State Tracking and Patient-Aware Planning (2026.acl-long)
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Xinwei Yang, Junyi Fan, Yuqing Liu, Jiaxuan Wang, Jiashuai Zhang, Hongru Liang, Wenqiang Lei, Yao Song
| Challenge: | Recent efforts have turned to large language models (LLMs) as therapeutic agents for psychological therapy tasks, yet robustness across diverse patients remains underexplored. |
| Approach: | They propose a realistic role-play protocol for evaluating therapeutic dialogue agents and a de-identified, expert-annotated corpus of therapist–patient dialogues. |
| Outcome: | The proposed framework outperforms baselines on therapeutic outcomes and dialogue quality while improving conversational efficiency. |
LLM-guided Plan and Retrieval: A Strategic Alignment for Interpretable User Satisfaction Estimation in Dialogue (2025.naacl-long)
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| Challenge: | Existing methods for estimating user satisfaction with dialogue systems face challenges due to limited understanding of underlying reasons for user dissatisfaction and high costs of annotating user intentions. |
| Approach: | They propose an interpretable framework for effective user satisfaction prediction . they propose to align utterances with strategies and large language models to retrieve relevant features from utterations. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three benchmarks for the USE task. |
DischargeSim: A Simulation Benchmark for Educational Doctor–Patient Communication at Discharge (2025.emnlp-main)
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| Challenge: | Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. |
| Approach: | They propose a benchmark that evaluates large language models’ ability to act as personalized discharge educators. |
| Outcome: | Experiments with 18 LLMs show that model size does not always yield better education outcomes, highlighting trade-offs in strategy use and content prioritization. |
Exploring Persona Sentiment Sensitivity in Personalized Dialogue Generation (2025.acl-long)
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| Challenge: | Personalized dialogue systems have advanced with the integration of user-specific personas into large language models (LLMs). |
| Approach: | They propose a dialogue generation approach that explicitly accounts for persona polarity by combining a turn-based generation strategy with a profile ordering mechanism and sentiment-aware prompting. |
| Outcome: | The proposed approach accounts for persona polarity by combining a turn-based generation strategy with a profile ordering mechanism and sentiment-aware prompting. |
3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark (2025.emnlp-main)
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Ivan Sviridov, Amina Miftakhova, Tereshchenko Artemiy Vladimirovich, Galina Zubkova, Pavel Blinov, Andrey Savchenko
| Challenge: | Large Vision-Language Models (LVLMs) are being explored in medicine but their ability to conduct complex real-world telemedicine consultations remains underexplored. |
| Approach: | They propose to use large vision-language models to conduct telemedicine consultations using a framework that simulates patient variability and evaluates diagnostic accuracy and dialogue quality via Assessor Agent. |
| Outcome: | The proposed framework compares diagnostic strategies for open and closed-source LVLMs and shows that multimodal dialogue improves F1 score by 6.5% over non-dialogue settings. |
Enhancing Goal-oriented Proactive Dialogue Systems via Dynamic Multi-dimensional Consistency Optimization (2025.findings-emnlp)
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| Challenge: | Existing work on goal-oriented proactive dialogue systems failed to address the multi-dimensional consistency issue between generated responses and key contextual elements. |
| Approach: | They propose a Dynamic Multi-dimensional Consistency Reinforcement Learning framework which measures the impact of each consistency dimension on overall dialogue quality and provides feedback to improve response quality. |
| Outcome: | The proposed framework significantly improves the consistency of generated responses on two datasets. |
AV-Dialog: Spoken Dialogue Models with Audio-Visual Input (2026.acl-long)
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| Challenge: | AV-Dialog uses audio and visual cues to track the target speaker, predict turn-taking, and generate coherent responses. |
| Approach: | They propose a multimodal dialog framework that uses both audio and visual cues to track the target speaker. |
| Outcome: | AV-Dialog outperforms audio-only models under interference, reducing transcription errors, improving turn-taking prediction and human-rated dialogue quality. |