DIALOGUE GENERATION

CDD: A Large Scale Dataset for Legal Intelligence Research (2023.emnlp-industry)

Challenge:	Existing methods to measure diversity of chitchat model responses have been proposed to measure iteratively.
Approach:	They propose a metric which uses Natural Language Inference to measure the semantic diversity of a set of model responses for a conversation.
Outcome:	The proposed metric improves the diversity of a sampled set of responses using a new generation procedure called Diversity Threshold Generation.

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Changzhen Ji, Yating Zhang, Adam Jatowt, Haipang Wu

Information Extraction & Text Mining

Challenge:	Recent research has focused on predicting crimes, predicting outcomes of judicial debates, and extracting information from legal documents.
Approach:	They propose to use a large-size Court Debate Dataset to analyze court debates . they invite experienced judges to design appropriate labels for data records .
Outcome:	The proposed dataset includes 30,481 court cases, totaling 1,144,425 utterances.

Automatic Dialogue Generation with Expressed Emotions (N18-2)

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Chenyang Huang, Osmar Zaïane, Amine Trabelsi, Nouha Dziri

Parameter-Efficient Conversational Recommender System as a Language Processing Task (2024.eacl-long)

Challenge:	a growing interest in neural dialogue generation systems is focusing on generating human-like responses based on past utterances . despite efforts, few consider putting restrictions on the response itself . authors present three models that concatenate the desired emotion with the source input .
Approach:	They propose three models that concatenate the desired emotion with the source input or push the emotion in the decoder.
Outcome:	The proposed model is more efficient than the previous models, but it lacks the emotion vector.

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Mathieu Ravaut, Hao Zhang, Lu Xu, Aixin Sun, Yong Liu

PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)

Challenge:	Existing methods to recommend items are categorized into attribute-based and generation-based methods.
Approach:	They propose to represent items in natural language and formulate a conversational recommender system that can be optimized in a single stage without relying on non-textual metadata.
Outcome:	The proposed model can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph.

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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu

Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)

Challenge:	Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks.
Approach:	They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations.
Outcome:	The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI.

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Libo Qin, Yijia Liu, Wanxiang Che, Haoyang Wen, Yangming Li, Ting Liu

Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks (2020.coling-industry)

Challenge:	Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other.
Approach:	They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history .
Outcome:	The proposed framework outperforms baseline models and produces entity-consistent responses.

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Zhengyuan Liu, Pavitra Krishnaswamy, Ai Ti Aw, Nancy Chen

Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue (2023.findings-acl)

Challenge:	Neural approaches have improved machine comprehension tasks, but models often operate as a black-box, resulting in lower interpretability.
Approach:	They propose a hybrid approach to quantify model uncertainty using Bayesian weight approximation and boost up inference speed by 80% relative to test time.
Outcome:	The proposed approach boosts inference speed by 80% relative to the previous approach and is applied to a clinical dialogue comprehension task.

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Jian Wang, Dongding Lin, Wenjie Li

KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding (2022.findings-acl)

Challenge:	Goal-directed dialogue systems aim to proactively reach a pre-determined target through multi-turn conversations.
Approach:	They propose a coherent dialogue planning approach that uses a stochastic process to model the temporal dynamics of dialogue paths.
Outcome:	The proposed approach generates more coherent utterances and achieves the goal with a higher success rate.

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Sixing Wu, Ying Li, Dawei Zhang, Zhonghai Wu

Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning (2020.findings-emnlp)

Challenge:	Existing knowledge-enhanced methods only seek knowledge from a single source, resulting in insufficient coverage of a given knowledge source.
Approach:	They propose to use multiple independent decoder heads to infuse multi-source knowledge into dialogue generation more efficiently by incorporating external knowledge into the dialogue generation.
Outcome:	The proposed approach overcomes three challenges in infusing multi-source knowledge into dialogue generation more efficiently.

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Yifan Gao, Piji Li, Wei Bi, Xiaojiang Liu, Michael Lyu, Irwin King

Long-term Control for Dialogue Generation: Methods and Evaluation (2022.naacl-main)

Challenge:	Sentence function is an important linguistic feature indicating the communicative purpose of a sentence in a conversation.
Approach:	They propose a structured meta-learning approach for dialogue generation on infrequent sentence functions.
Outcome:	The proposed approach improves informativeness and relevance of dialogue generation on infrequent sentence functions while preserving knowledge generalization for similar sentence functions.

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Ramya Ramakrishnan, Hashan Narangodage, Mauro Schilman, Kilian Weinberger, Ryan McDonald

MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)

Challenge:	Current approaches for controlling dialogue response generation focus on high-level attributes like style, sentiment, or topic.
Approach:	They propose a method that allows for more fine-grained control of dialogue response generation . they propose utterances that encourage the generation of control words in the future .
Outcome:	The proposed method outperforms state-of-the-art constrained generation baselines on task-oriented dialogue datasets and shows that it is more fine-grained than previous methods.

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Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang

Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation (2023.emnlp-main)

Challenge:	Existing zero-shot dialogue generation systems rely on large-scale pre-trained language models.
Approach:	They propose a multilingual learning framework for zero-shot dialogue generation that can transfer knowledge from an English corpus to a non-English corpus with zero samples.
Outcome:	The proposed framework can transfer knowledge from an English corpus to a non-English corpus with zero samples.

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Wei-Lin Chen, Cheng-Kuang Wu, Hsin-Hsi Chen, Chung-Chi Chen

BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla (2023.findings-eacl)

Challenge:	Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies.
Approach:	They propose a new decoding method that augments the contrastive search framework with context-aware regularization terms to promote tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text.
Outcome:	The proposed method improves faithfulness across various language models while maintaining output diversity comparable to well-performing decoding algorithms.

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Abhik Bhattacharjee, Tahmid Hasan, Wasi Uddin Ahmad, Rifat Shahriyar

First-AID: the first Annotation Interface for grounded Dialogues (2025.acl-demo)

Challenge:	'BanglaNLG' is a comprehensive benchmark for evaluating natural language generation models in Bangla, a widely spoken yet low-resource language.
Approach:	They propose to aggregate six conditional text generation tasks under the BanglaNLG benchmark and introduce a new dataset on dialogue generation in the process.
Outcome:	The proposed model outperforms several multilingual models by 9% absolute gain and 32% relative gain on all of these tasks.

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Stefano Menini, Daniel Russo, Alessio Palmero Aprosio, Marco Guerini

Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys (2024.findings-eacl)

Challenge:	Existing tools to fine-tune Large Language Models for specific tasks are limited due to financial constraints and limited availability of human experts.
Approach:	They propose a human-in-the-loop framework for the knowledge-driven generation of synthetic dialogues using LLM prompting that implements different strategies of data collection that require different user intervention during dialogue generation.
Outcome:	The proposed framework reduces post-editing efforts and improves quality of generated dialogues.

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Yong Cao, Min Chen, Daniel Hershcovich

Group-wise Contrastive Learning for Neural Dialogue Generation (2020.findings-emnlp)

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.

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Hengyi Cai, Hongshen Chen, Yonghao Song, Zhuoye Ding, Yongjun Bao, Weipeng Yan, Xiaofang Zhao

How do decoding algorithms distribute information in dialogue responses? (2023.findings-eacl)

Challenge:	Existing approaches to training dialogue models have low diversity in open-domain contexts . prior art suggests that naive MLE objective is not effective enough .
Approach:	They propose to incorporate contrastive learning into dialogue generation by using a pretrained baseline model as a reference.
Outcome:	The proposed framework is suited for training a wide range of dialogue generation models with favorable performance over baseline training approaches.

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Saranya Venkatraman, He He, David Reitter

An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (D18-1)

Challenge:	Using different decoding algorithms, we find that human dialogue generation is beneficial for adherence to the Uniform Information Density principle.
Approach:	They investigate whether decoding algorithms implicitly follow the Uniform Information Density principle by distributing information evenly in utterances.
Outcome:	The proposed method encourages non-uniform responses, but under low/high surprisal conditions, resulting in poor quality responses.

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Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, Xu Sun

Designing Multilingual Interactive Agents using Small Dialogue Corpora (2020.lrec-1)

Challenge:	Experimental results show that our model can generate semantically coherent responses compared to baseline models.
Approach:	They propose an Auto-Encoder Matching model to learn utterance-level semantic dependency . their model contains two auto-encoders and one mapping module .
Outcome:	Experimental results show that the proposed model can generate high coherence and fluency compared to baseline models.

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Donghui Lin, Masayuki Otani, Ryosuke Okuno, Toru Ishida

FaithDial: A Faithful Benchmark for Information-Seeking Dialogue (2022.tacl-1)

Challenge:	a new study aims to develop a design framework for multilingual interactive agents . large amounts of data and language resources are needed to develop most key components .
Approach:	They propose a general design framework for multilingual interactive agents in specialized domains with small or non-existent dialogue corpora.
Outcome:	The proposed framework integrates external language services for supporting multilingual functions and realizes context-aware dialogue generation under the situation of small corpora.

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Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar Zaiane, Mo Yu, Edoardo M. Ponti, Siva Reddy

Dolphin: A Challenging and Diverse Benchmark for Arabic NLG (2023.findings-emnlp)

Challenge:	a new benchmark for hallucination-free dialogues is based on knowledge-based conversational models that generate unsupported utterances . a recent study shows that models that are trustworthy generate unverifiable or factually incorrect statements .
Approach:	They propose a data-centric solution to edit hallucinated responses in the Wizard of Wikipedia benchmark.
Outcome:	The proposed model improves on the Wizard of Wikipedia benchmark while maintaining engaging conversations.

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El Moatez Billah Nagoudi, AbdelRahim Elmadany, Ahmed El-Shangiti, Muhammad Abdul-Mageed

White-Box Multi-Objective Adversarial Attack on Dialogue Generation (2023.acl-long)

Challenge:	Existing benchmarks for Arabic are limited, but they can be used to measure performance of different languages.
Approach:	They propose a benchmark for Arabic that addresses the need for a framework dedicated to Arabic languages and varieties.
Outcome:	The proposed benchmark covers 13 different tasks in Arabic and spans 50 test splits.

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Yufei Li, Zexin Li, Yingfan Gao, Cong Liu

Unsupervised Knowledge Selection for Dialogue Generation (2021.findings-acl)

Challenge:	Pre-trained transformers are popular in state-of-the-art dialogue generation systems . however, they are vulnerable to adversarial samples crafted by small and imperceptible perturbations.
Approach:	They propose a multi-objective attack method that balances two objectives: generation accuracy and length.
Outcome:	The proposed method significantly degrades state-of-the-art DG models with a higher success rate than traditional accuracy-based methods.

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Xiuyi Chen, Feilong Chen, Fandong Meng, Peng Li, Jie Zhou

M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions (2024.acl-long)

Challenge:	Existing knowledge selection tasks require the preidentified knowledge to generate informative dialogues.
Approach:	They propose a novel method to supervise knowledge selection when the gold knowledge label is unknown by obtaining an oracle knowledge label via distant supervision and leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision.
Outcome:	The proposed method outperforms strong supervised baselines on two knowledge-grounded dialogue datasets and generates more informative responses.

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Zheng Wang, Shu Teo, Jieer Ouyang, Yongjun Xu, Wei Shi

Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)

Challenge:	Existing methods for retrieving relevant memories from an external database are coarse-grained and can cause noise and focus on crucial memories.
Approach:	They propose a multiple partition paradigm for RAG where each database partition serves as a basic unit for execution.
Outcome:	The proposed framework outperforms baseline methods on three language generation tasks on seven datasets.

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Siheng Li, Wangjie Jiang, Pengda Si, Cheng Yang, Qiu Yao, Jinchao Zhang, Jie Zhou, Yujiu Yang

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)

Challenge:	Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation.
Approach:	They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph.
Outcome:	The proposed method performs better than baselines on a large-scale reddit conversation dataset.

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Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Huang Hu, Xiubo Geng, Daxin Jiang

Automatically Learning Data Augmentation Policies for Dialogue Tasks (D19-1)

Challenge:	Generating natural and informative texts has been a long-standing problem in NLP.
Approach:	They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework.
Outcome:	The proposed model can learn what and how to generate on two text generation tasks.

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Tong Niu, Mohit Bansal

Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies (2025.coling-main)

Challenge:	Recent advances in automatic data augmentation have focused on computer vision tasks where it is easy to apply imperceptible perturbations without changing an image’s semantic meaning.
Approach:	They adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation.
Outcome:	The proposed algorithm reduces data-level model bias by using a controller trained on the target task.

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Xin Sun, Xiao Tang, Abdallah El Ali, Zhuying Li, Pengjie Ren, Jan de Wit, Jiahuan Pei, Jos A.Bosch

A Unifying View On Task-oriented Dialogue Annotation (2022.lrec-1)

Challenge:	Motivational interviewing (MI) is a client-centered counseling technique that encourages individuals to change behaviors through emphatic conversations.
Approach:	They propose to use large language models to generate more controllable dialogues with explainability by prompting LLMs to predict appropriate strategies as reasoning and utilizing these strategies to guide dialogue generation.
Outcome:	The proposed model generates more controllable and explainable dialogues with a set of MI skills.

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Vojtěch Hudeček, Leon-paul Schaub, Daniel Stancl, Patrick Paroubek, Ondřej Dušek

Knowledge Diffusion for Neural Dialogue Generation (P18-1)

Challenge:	Recent research attention in task-oriented dialogue systems focuses on end-to-end neural models.
Approach:	They present a dataset that combines annotated corpora from four domains to provide a unified ontology and annotation schema for task-oriented dialogues.
Outcome:	The proposed dataset improves language, information content and performance in dialogues with two recent models.

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Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin

VishBox v2: A Multi-Agent System for Adaptive Voice Phishing Simulation (2026.acl-industry)

Challenge:	End-to-end neural dialogue generation does not employ knowledge to guide the generation.
Approach:	They propose a neural knowledge diffusion model to introduce knowledge into dialogue generation.
Outcome:	The proposed model outperforms baseline models on a real-world dataset.

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Sungmi Park, Daon Choi, Yoonmo Yang, Hong Yunyi, Heedou Kim

Task-Level Thinking Steps Help Large Language Models for Challenging Classification Task (2023.emnlp-main)

Challenge:	Existing alternatives lack round-level granularity and controllability, making it difficult to analyze how victim profiles and psychological states shape tactic effectiveness.
Approach:	They propose a multi-agent architecture that generates structured phishing simulations grounded in crime-script procedures and persuasion principles.
Outcome:	The proposed framework captures tactic concentration, vulnerability transitions, and web-search-induced procedural disruptions across 571 rounds.

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Chunhui Du, Jidong Tian, Haoran Liao, Jindou Chen, Hao He, Yaohui Jin

Attribute Controlled Dialogue Prompting (2023.findings-acl)

Challenge:	Experimental results prove the superiority of our proposed method on challenging classification tasks.
Approach:	They propose a task-level thinking step that eliminates bias introduced by demonstrations . they propose 'progressive revision framework' which can improve the thinking steps by correcting hard demonstrations.
Outcome:	The proposed method achieves best performance on three kinds of classification tasks in zero-shot and few-shot settings.

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Runcheng Liu, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, Pascal Poupart

G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment (2023.emnlp-main)

Challenge:	Prompt-tuning is an increasingly popular parameter-efficient method for adapting large pretrained language models to downstream tasks.
Approach:	They propose an instance-specific prompt-tuning algorithm for dialog generation that generates prompts based on instance-level control code rather than the conversation history.
Outcome:	The proposed prompt-tuning module is a fraction of the size of the pretrained language model and saves memory and expensive storage space.

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Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu

RefGPT: Dialogue Generation of GPT, by GPT, and for GPT (2023.findings-emnlp)

Challenge:	Conventional reference-based metrics have low correlation with human judgments, especially for open-ended generation tasks.
Approach:	They propose to use large language models as reference-free NLG evaluators to assess the quality of NLG outputs.
Outcome:	The proposed framework outperforms all previous methods in two generation tasks, and has a Spearman correlation of 0.514 with human on summarization task, and a large variance in human judgments.

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Dongjie Yang, Ruifeng Yuan, Yuantao Fan, Yifei Yang, Zili Wang, Shusen Wang, Hai Zhao

Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have impressive capability to resolve a wide range of NLP tasks by fine-tuning high-quality instruction data.
Approach:	They propose a method to generate huge truthful and customized dialogues without worrying about factual errors caused by the model hallucination.
Outcome:	The proposed method solves the model hallucination in dialogue generation by restricting the LLMs to leverage the given reference instead of reciting their own knowledge to generate dialogues.

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Leyang Cui, Yu Wu, Shujie Liu, Yue Zhang

Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)

Challenge:	Existing methods for dialogue generation use an external knowledge base to generate appropriate responses.
Approach:	They propose to use an external knowledge base to generate appropriate responses for unseen entities.
Outcome:	Experiments on two dialogue corpus show that pre-trained models perform poorly with unseen entities.

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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si

Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks (D19-1)

Challenge:	Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular.
Approach:	They propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information to generate generic seller responses.
Outcome:	The proposed model can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.

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Igor Shalyminov, Sungjin Lee, Arash Eshghi, Oliver Lemon

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)

Challenge:	Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains.
Approach:	They propose a data-driven approach to goal-oriented dialogue generation which only uses a few example dialogues, none of which has to be annotated.
Outcome:	The proposed approach significantly improves upon baseline models and over the previous state-of-the-art model, ZSDG.

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Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang, Ruihui Zhao, Bang Liu, Xiaodan Liang, Yefeng Zheng

DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation (2022.findings-naacl)

Challenge:	Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems.
Approach:	They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies.
Outcome:	The proposed system significantly outperforms baselines in both dialogue generation and strategy planning.

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Md Rashad Al Hasan Rony, Ricardo Usbeck, Jens Lehmann

Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue (2024.acl-long)

Challenge:	Recent research focused on knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured.
Approach:	They propose a novel task-oriented dialogue system that effectively incorporates knowledge into a language model by using structural information of a knowledge graph.
Outcome:	The proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a Knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation.

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Jian Wang, Chak Tou Leong, Jiashuo Wang, Dongding Lin, Wenjie Li, Xiaoyong Wei

A Batch Normalized Inference Network Keeps the KL Vanishing Away (2020.acl-main)

Challenge:	Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents.
Approach:	They propose a multi-round interactive dialogue tuning framework that models the speaker roles of agent and user separately.
Outcome:	The proposed framework performs superior to fine-tuning and improves dialogue consistency.

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Qile Zhu, Wei Bi, Xiaojiang Liu, Xiyao Ma, Xiaolin Li, Dapeng Wu

Challenge:	Variational Autoencoder (VAE) is widely used to approximate a model’s posterior on latent variables.
Approach:	They propose to let the Kullback–Leibler divergence individual follow a distribution across the whole dataset and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL’s distribution positive.
Outcome:	The proposed approach can avoid posterior collapse effectively and efficiently without introducing any new model component or modifying the objective.

Controlling Dialogue Generation with Semantic Exemplars (2021.naacl-main)

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Prakhar Gupta, Jeffrey Bigham, Yulia Tsvetkov, Amy Pavel

Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue (2024.findings-naacl)

Challenge:	Existing methods to control dialogue generation are manual labelling and manual editing of data.
Approach:	They propose a method to control dialogue generation using exemplar responses . they use semantic frames present in exemplars to guide response generation .
Outcome:	The proposed model improves coherence while preserving semantic meaning and conversation goals . exemplar responses are handwritten or strategically curated to promote highlevel goals without explicit labels .

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Junkai Zhou, Liang Pang, Huawei Shen, Xueqi Cheng

Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation (2023.acl-long)

Challenge:	emergence of large language models (LLMs) improves capabilities of dialogue systems . but they lack communication skills, which make them more like information seeking tools .
Approach:	They propose to empower LLMs with communication skills through inner monologues . they use a benchmark to evaluate the dialogue generation ability of the model .
Outcome:	The proposed model outperforms the baselines in the evaluation of communication skills.

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Chen Tang, Hongbo Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

Event Transition Planning for Open-ended Text Generation (2022.findings-acl)

Challenge:	Existing graph neural networks (GNNs) teach message passing on a graph from text, resulting in a semantic gap between graph knowledge and text.
Approach:	They propose a framework to integrate external graph knowledge into chatbots by coagulating representations of both text and graph knowledge.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) baselines on dialogue generation.

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Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, Lingpeng Kong

Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)

Challenge:	Open-ended text generation tasks require models to generate coherent continuation given limited preceding context.
Approach:	They propose a novel two-stage method which explicitly arranges ensuing events in open-ended text generation tasks.
Outcome:	The proposed method improves coherence and diversity of open-ended text generation tasks.

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Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, Rui Yan

Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020.emnlp-main)

Challenge:	Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.
Approach:	They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues.
Outcome:	The proposed model outperforms state-of-the-art methods in evaluation and human judgment.

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Xiuyi Chen, Fandong Meng, Peng Li, Feilong Chen, Shuang Xu, Bo Xu, Jie Zhou

Towards Controllable Biases in Language Generation (2020.findings-emnlp)

Challenge:	Existing knowledge-grounded dialogue models lack prior and posterior knowledge selection . prior selection module may not learn to select knowledge properly because of lack of posterior information .
Approach:	They propose a knowledge distillation-based training strategy to remove the exposure bias of knowledge selection.
Outcome:	The proposed model improves on two knowledge-grounded dialogue datasets.

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Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng

Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona (2023.acl-long)

Challenge:	a new method to induce societal biases in natural language generation is being developed . a method to equalize the amount of biased text across demographics is effective .
Approach:	They propose a method to induce societal biases in natural language generation by using demographic inequalities.
Outcome:	The proposed method is effective at equalizing biases across demographics while generating less negatively biased text overall.

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Yihong Tang, Bo Wang, Miao Fang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation (2022.acl-long)

Challenge:	Existing personalized dialogue agents model persona profiles from sparse or dense persona descriptions and dialogue histories.
Approach:	They propose a model that clusters dense persona descriptions into sparse categories and generates personalized responses from dialogue histories.
Outcome:	The proposed model improves on Chinese and English datasets.

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Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan

On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond (2020.acl-main)

Challenge:	Existing studies focus on contrastive learning on the instance level without discriminating the contribution of each word.
Approach:	They propose a hierarchical contrastive learning mechanism which can unify semantic meaning in the input text.
Outcome:	The proposed model outperforms baselines on storytelling, paraphrasing, dialogue generation, and storytelling tasks.

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Chen Wu, Prince Zizhuang Wang, William Yang Wang

LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback (2024.naacl-long)

Challenge:	Existing work has shown that the optimization of variational autoencoders suffers from the posterior collapse problem.
Approach:	They propose a variational autoencoder that couples a VAE model with a deterministic autoencoding model and improves the parameters via weight sharing and decoder signal matching.
Outcome:	The proposed model improves on benchmark datasets and improves diversity of dialogue generation.

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Guanghui Ye, Huan Zhao, Zixing Zhang, Xupeng Zha, Zhihua Jiang

Sparse Text Generation (2020.emnlp-main)

Challenge:	Existing dialogue systems do not utilize quality dimensions specifically designed for dialogue evaluation to guide the response generation during training.
Approach:	They propose a two-stage framework which generates and utilizes conversation evaluation as explicit feedback during training.
Outcome:	The proposed framework generates and utilizes conversation evaluation as explicit feedback during training.

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Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Large Language Models Are No Longer Shallow Parsers (2024.acl-long)

Challenge:	Current text generators require sampling from a modified softmax to avoid degenerate text . entmax sampling creates a mismatch between training and testing conditions .
Approach:	They propose to use entmax transformation to train and sample from a sparse language model to avoid degenerate text.
Outcome:	The proposed model improves fluency and consistency, fewer repetitions, and n-gram diversity closer to human text.

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Yuanhe Tian, Fei Xia, Yan Song

ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences (2024.acl-long)

Challenge:	Recent advances in large language models (LLMs) have reshaped the field of natural language processing (NLP) however, fundamental NLP tasks that involve linguistic analysis still play essential roles in the field.
Approach:	They propose to use constituency parsing to improve performance of LLMs on deep syntactic parse trees to prompt LLM chunking, filter out low-quality chunks and add remaining chunks to prompts to instruct LLM for parser.
Outcome:	The proposed approach improves LLMs' performance on constituency parsing on English and Chinese benchmark datasets.

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Yuanhe Tian, Ruyi Gan, Yan Song, Jiaxing Zhang, Yongdong Zhang

He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation (2021.findings-acl)

Challenge:	Current large language models (LLMs) are ineffective in learning domain knowledge and aligning with human preference.
Approach:	They propose a benchmark LLM for Chinese medical domain that uses pre-training, supervised fine-tuning and RLHF to train LLMs.
Outcome:	The proposed LLM performs better than existing LLMs in the Chinese medical domain.

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Aparna Garimella, Akhash Amarnath, Kiran Kumar, Akash Pramod Yalla, Anandhavelu N, Niyati Chhaya, Balaji Vasan Srinivasan

Deep Reinforcement Learning with Hierarchical Action Exploration for Dialogue Generation (2024.lrec-main)

Challenge:	Existing studies have focused on mitigating social biases in context-free representations, with recent shift to contextual ones.
Approach:	They propose an approach to mitigate social biases in a large pre-trained contextual language model . they propose lexical co-occurrence-based bias penalization in the decoder units .
Outcome:	The proposed approach reduces biases in fill-in-the-blank sentences and summarizes . it also reduces the biased representations in the frameworks, the authors show .

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Itsugun Cho, Ryota Takahashi, Yusaku Yanase, Hiroaki Saito

SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation (2026.findings-acl)

Challenge:	Existing approaches to improve dialogues with random sampling are inefficient due to the large number of eligible responses with high action values.
Approach:	They propose a dual-granularity Q-function that extracts actions based on a grained hierarchy . they use offline RL and learn from multiple reward functions designed to capture emotional nuances in human interactions.
Outcome:	The proposed approach outperforms baselines across automatic metrics and human evaluations.

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Han Luo, Guy Laban

Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness (2021.emnlp-main)

Challenge:	Large language models are increasingly deployed in multi-turn settings such as tutoring, support, and counseling where reliability depends on preserving consistent roles, personas, and goals across long horizons.
Approach:	They propose a framework that decomposes LLM–LLM conversations into a modular, stability-first framework that allows for a stable persona-driven agent simulation for multi-turn dialogue generation.
Outcome:	The proposed framework decomposes the LLM-based model into four main components: persona creation, plausibility validation, and natural-language persona crafting.

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Hengtong Zhang, Tianhang Zheng, Yaliang Li, Jing Gao, Lu Su, Bo Li

Robustness in NLP

Challenge:	a recent study shows that inappropriate language can cause models to output profanity . authors propose a training framework to prevent such outputs from hurting the usability of models .
Approach:	proposed training framework eliminates the causes that trigger the generation of profanity . authors propose a framework that leverages a short list of profans to prevent this .
Outcome:	a proposed training framework can prevent models from generating profanity . the proposed framework leverages a short list of profanities examples .

AirDialogue: An Environment for Goal-Oriented Dialogue Research (D18-1)

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Wei Wei, Quoc Le, Andrew Dai, Jia Li

DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space (2024.lrec-main)

Challenge:	Recent advances in dialogue generation have inspired a number of studies on dialogue systems . however, current datasets are limited in size and the environment for training agents is relatively unsophisticated.
Approach:	They propose to use a context-generator to generate travel and flight restrictions to train agents.
Outcome:	The proposed model achieves a score of 0.17 while humans can reach 0.91 . the proposed model is based on a large dataset that contains 301,427 goal-oriented conversations .

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Jianxiang Xiang, Zhenhua Liu, Haodong Liu, Yin Bai, Jia Cheng, Wenliang Chen

More Diverse Dialogue Datasets via Diversity-Informed Data Collection (2020.acl-main)

Challenge:	Existing studies have tried to introduce discrete or Gaussian-based latent variables to address the one-to-many problem, but the diversity is limited.
Approach:	They propose a diffusion model to enhance the diversity of dialogue generation by using continuous latent variables instead of discrete ones.
Outcome:	The proposed model greatly enhances diversity of dialog response while keeping the coherence.

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Katherine Stasaski, Grace Hui Yang, Marti A. Hearst

VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making (2025.emnlp-main)

Challenge:	Existing approaches to generate conversational dialogue produce uninteresting, predictable responses.
Approach:	They propose a method to collect and determine more diverse data from conversational participants . they use dynamically computed corpus-level statistics to determine which conversational participant to collect data from .
Outcome:	The proposed method produces significantly more diverse data than baseline methods and better results on emotion classification and dialogue generation tasks.

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Zuojin Tang, Bin Hu, Chenyang Zhao, De Ma, Gang Pan, Bin Liu

Multimodality

Challenge:	Recent large-scale pretrained models are built upon a multi-input single-output paradigm . tasks compete for a shared output channel, creating mutual exclusion effects .
Approach:	They propose a multi-input single-output (MISO) paradigm for large pretrained models . they propose unified training framework that enables concurrent multi-task outputs .
Outcome:	Experiments on autonomous driving platform show that MIMO-VLA outperforms state-of-the-art models in MIMO settings.

RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy (2021.acl-long)

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Xiyan Fu, Yating Zhang, Tianyi Wang, Xiaozhong Liu, Changlong Sun, Zhenglu Yang

X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (2024.naacl-long)

Challenge:	Existing methods to learn vital information from dialogue context with limited data are limited due to limited words in utterances and huge gap between dialogue and its summary.
Approach:	They propose an unsupervised strategy to learn vital information from dialogue context . the proposed model uses a hypothetical foundation that a superior summary approximates a replacement of the original dialogue .
Outcome:	The proposed model outperforms existing models on a number of datasets.

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Minqian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar, Reza Ghanadan, Lifu Huang

Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (2020.acl-main)

Challenge:	X-Eval is a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users.
Approach:	They introduce a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users.
Outcome:	The proposed framework improves the model’s ability to follow evaluation instructions and enhances the learning stage to better assess text quality.

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Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu

Neural Response Generation with Meta-words (P19-1)

Challenge:	Generative dialogue systems tend to produce generic and boring responses, causing boring conversations . a novel commonsense knowledge-aware dialogue generation model is proposed to solve this problem .
Approach:	They propose to retrieve and introduce knowledge facts from knowledge graphs to reduce boring conversations . they use a Felicitous Fact mechanism to help the model focus on context-relevant knowledge facts .
Outcome:	The proposed model outperforms the state-of-the-art approach in most experiments.

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Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, Ying Wang

DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering (2023.acl-long)

Challenge:	Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP .
Approach:	They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller.
Outcome:	The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy.

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Pei Ke, Fei Huang, Fei Mi, Yasheng Wang, Qun Liu, Xiaoyan Zhu, Minlie Huang

PEK: A Parameter-Efficient Framework for Knowledge-Grounded Dialogue Generation (2024.findings-acl)

Challenge:	Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability.
Approach:	They propose a metric that evaluates natural language generation tasks as an instruction-style question answering task and utilizes instruction-tuned pre-trained language models without training on evaluation datasets.
Outcome:	The proposed metric achieves state-of-the-art performance in untrained metrics for evaluating text summarization and dialogue generation, which exhibits strong dimension-level / task-level generalization ability and interpretability.

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Pan Yang, Dandan Song, Zhijing Wu, Yanru Zhou

Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020.acl-main)

Challenge:	Pre-trained language models have shown great dialogue generation capability in different scenarios, but the huge VRAM consumption when fine-tuning them is one of their drawbacks.
Approach:	They propose a parameter-efficient framework for knowledge-enhanced dialogue generation that leverages external knowledge documents and knowledge graphs to enhance its generation capabilities.
Outcome:	The proposed framework outperforms baseline methods on multiple evaluation metrics on Wizard of Wikipedia and CMU_DoG datasets.

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Hengyi Cai, Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao, Dawei Yin

DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)

Challenge:	Current state-of-the-art neural dialogue models learn from human conversations . however, due to the open-ended nature of human conversations, the quality of training data varies .
Approach:	They propose a data manipulation framework to augment and highlight effective training samples . they also propose to increase its manipulation skills through gradient descent with validation samples a reshaping framework to proactively restructure the data distribution towards reliable samples is also proposed .
Outcome:	The proposed framework improves the performance of open-domain neural dialogue models with respect to evaluation metrics and human judgments.

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Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, Yongbin Li

Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)

Challenge:	Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction.
Approach:	They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning.
Outcome:	The proposed agent performs well in both dialogue element modeling and out-of-domain tasks.

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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang

Diversifying Dialogue Generation with Non-Conversational Text (2020.acl-main)

Challenge:	Recent advances in neural models have shown promising progress on this task, but key challenges remain .
Approach:	They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template .
Outcome:	The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial.

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Hui Su, Xiaoyu Shen, Sanqiang Zhao, Zhou Xiao, Pengwei Hu, Randy Zhong, Cheng Niu, Jie Zhou

FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation (2023.findings-emnlp)

Challenge:	Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation.
Approach:	They propose a way to diversify dialogue generation by leveraging non-conversational text . they collect large-scale corpus from forum comments, idioms and book snippets .
Outcome:	The proposed model produces significantly more diverse responses without sacrificing relevance with context.

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Kun Zhang, Oana Balalau, Ioana Manolescu

Structured Data in NLP

Challenge:	Graph-to-text (G2T) generation is an important task in natural language generation as it renders graphs accessible to non-technical users in downstream applications such as question answering.
Approach:	They propose a metric that correctly identifies factual faithfulness and uses it to determine if a triple is present in a generated text.
Outcome:	The proposed metric achieves highest correlation with human annotations on data correctness, data coverage, and relevance.

We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses (2023.emnlp-main)

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Benjamin Kane, Lenhart Schubert

Natural Language Interfaces

Challenge:	a variety of personas can be elicited from large language models, but they are opaque and unpredictable.
Approach:	They propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses.
Outcome:	The proposed method captures habitual knowledge and generates persona-based responses from a large language model.

Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure (2022.emnlp-main)

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Xueliang Zhao, Lemao Liu, Tingchen Fu, Shuming Shi, Dongyan Zhao, Rui Yan

HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023.acl-long)

Challenge:	Existing models that use millions of parameters on massive data are inefficient and lack interpretability.
Approach:	They propose a model with a latent structure that is easily transferable from the general domain to downstream tasks in a lightweight and transparent way.
Outcome:	The proposed model performs better than four strong baseline models in terms of automatic and human evaluations and is 5x faster than the strongest baseline model.

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Qianyu He, Yikai Zhang, Jiaqing Liang, Yuncheng Huang, Yanghua Xiao, Yunwen Chen

Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation (2022.emnlp-main)

Challenge:	Similes are a crucial part of creative writing, but there is still a lack of evaluation metrics for simile generation.
Approach:	They propose to use similes as a tool to evaluate simile generation metrics . they propose to combine five criteria and automatic metrics for each criterion .
Outcome:	The proposed metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics.

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Yanyang Li, Jianqiao Zhao, Michael Lyu, Liwei Wang

Low-Resource NLP

Challenge:	Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text.
Approach:	They propose a posterior-based reweighing and noisy training strategy to exploit generated knowledge in dialogue generation.
Outcome:	Empirical results show that the proposed methods outperform the state-of-the-art methods in unsupervised knowledge-grounded conversation.

ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024.acl-long)

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Tenggan Zhang, Xinjie Zhang, Jinming Zhao, Li Zhou, Qin Jin

MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)

Challenge:	Emotion-focused and strategy-driven chain-of-thought (ESCoT) is a new paradigm for emotional support dialogues.
Approach:	They propose an emotional support response generation scheme to improve interpretability . they generate a dataset and develop a model to generate dialogue responses with better interpretability.
Outcome:	The proposed scheme can generate dialogue responses with better interpretability.

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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie

On the Compositional Generalization in Versatile Open-domain Dialogue (2023.acl-long)

Challenge:	telemedicine is a medical practice that provides patient care remotely using video conferencing tools.
Approach:	They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance .
Outcome:	The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues.

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Tingchen Fu, Xueliang Zhao, Lemao Liu, Rui Yan

CausalDialogue: Modeling Utterance-level Causality in Conversations (2023.findings-acl)

Challenge:	Existing approaches to multi-task learning suffer from interference among datasets or fail to effectively reuse knowledge and skills learned from other datasets.
Approach:	They propose a sparsely activated modular network with a well-rounded set of operators and instantiate each operator with an independent module.
Outcome:	The proposed model outperforms state-of-the-art supervised approaches on 4 datasets with only 10% training data thanks to the modular architecture and multi-task learning.

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Yi-Lin Tuan, Alon Albalak, Wenda Xu, Michael Saxon, Connor Pryor, Lise Getoor, William Yang Wang

Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)

Challenge:	Despite widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans . despite their widespread adoption in society, chatbots have yet not shown natural chat capability .
Approach:	They propose a causality-enhanced method to enhance the impact of causality at the utterance level in training neural conversation models.
Outcome:	The proposed method improves diversity and agility of loss functions and still needs improvement . the proposed method is based on a CausalDialogue dataset .

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Weihao Zeng, Lulu Zhao, Keqing He, Ruotong Geng, Jingang Wang, Wei Wu, Weiran Xu

Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information (2025.findings-emnlp)

Challenge:	Existing controllable dialogue generation models focus on single attribute and lack generalization capability to out-of-distribution multiple attribute combinations.
Approach:	They propose a compositional generalization model that learns from seen attributes and generalizes to unseen combinations.
Outcome:	The proposed model can learn from seen attribute values and generalize to unseen combinations.

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Weicheng Ma, Hefan Zhang, Shiyu Ji, Farnoosh Hashemi, Qichao Wang, Ivory Yang, Joice Chen, Juanwen Pan, Michael Macy, Saeed Hassanpour, Soroush Vosoughi

When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation (2025.acl-long)

Challenge:	Existing approaches to persuasive dialogue generation suffer from stance oscillation and low informativeness.
Approach:	They propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation.
Outcome:	The proposed method ensures speaker characteristics guide all stages of dialogue generation and aligns language use with speakers’ native languages to better capture cultural nuances.

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Daniela Occhipinti, Marco Guerini, Malvina Nissim

NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)

Challenge:	In recent years, large language models (LLMs) have proven effective in generating coherent and contextually appropriate responses.
Approach:	They examine the ability of a model to adapt to the interlocutor's profile by masking or disclosing information about interlucutor .
Outcome:	The proposed model generalises well across topics, but struggles with unfamiliar interlocutors.

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Junda Wang, Zonghai Yao, Zhichao Yang, Huixue Zhou, Rumeng Li, Xun Wang, Yucheng Xu, Hong Yu

LLMR: Knowledge Distillation with a Large Language Model-Induced Reward (2024.lrec-main)

Challenge:	NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps .
Approach:	They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues.
Outcome:	The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout .

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Dongheng Li, Yongchang Hao, Lili Mou

Exploring the Effectiveness of Multi-Lingual Commonsense Knowledge-Aware Open-Domain Dialogue Response Generation (2023.findings-emnlp)

Challenge:	Large language models have demonstrated remarkable performance in various NLP tasks, but are typically computationally expensive and difficult to be deployed in resource-constrained environments.
Approach:	They propose a knowledge distillation method based on a reward function induced from large language models.
Outcome:	The proposed method outperforms traditional methods on multiple datasets and tasks.

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Sixing Wu, Jiong Yu, Tianshi Che, Yang Zhou, Wei Zhou

Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition (2025.findings-emnlp)

Challenge:	Existing studies have shown that commonsense knowledge-aware models can improve informativeness while reducing the hallucination issue.
Approach:	They propose a task to use commonsense knowledge in other languages to enhance the current dialogue generation by using commonsensical knowledge in different languages.
Outcome:	The proposed model improves the current dialogue generation while reducing the hallucination issue.

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Dong Won Lee, Hae Won Park, Cynthia Breazeal, Louis-Philippe Morency

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling (2026.acl-long)

Challenge:	a large language model is used to decompose global feedback into a lightweight reward model.
Approach:	They propose a large language model based reward decomposition framework for dialogue agents . they use a frozen large language modeling framework to decompose global feedback .
Outcome:	The proposed framework infers fine-grained local rewards from a single session-level feedback signal.

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Hao-Xiang Xu, Chong Deng, Jiaqing Liu, Wen Wang, Qian Chen, Lujia Bao, Xiangang Li, Zhen-Hua Ling

Challenge:	Large Language Models (LLMs) extend their capabilities through function-calling (FC) however, obtaining and annotating real function-called data is challenging, and synthetic data from existing pipelines suffers from unreliable APIs, limited tool scalability, insufficient diversity, and weak quality control.
Approach:	They propose a pipeline for generating FC training data using reliable tools and a multi-agent framework that supports a dialogue generation system that produces conversations spanning diverse scenarios.
Outcome:	The proposed pipeline outperforms open-source models in in-domain FC performance and out-of-domain generalization while reaching FC capabilities comparable to some of the latest API-based models.

MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion (2026.findings-acl)

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Dingyi Zhang, Ziqing Zhuang, Linhai Zhang, Ziyang Gao, Deyu Zhou

Reasoning

Challenge:	Existing approaches to persuasion generate generic or weakly grounded responses even when such cues are identified.
Approach:	They propose a meta-cognitive autonomous intelligent agent framework for complex persuasion that coordinates perception management, mental-state inference, strategy execution, memory maintenance, and performance evaluation.
Outcome:	The proposed framework achieves a higher persuasion success rate than baselines.

NormGenesis: Multicultural Dialogue Generation via Exemplar-Guided Social Norm Modeling and Violation Recovery (2025.emnlp-main)

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Minki Hong, Jangho Choi, Jihie Kim

ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)

Challenge:	Social norms govern culturally appropriate behavior in communication, enabling dialogue systems to produce coherent and socially acceptable outputs.
Approach:	They propose a framework for generating and annotating socially grounded dialogues in Chinese, English, and Korean.
Outcome:	The proposed framework outperforms existing frameworks in refinement quality, dialogue naturalness, and generalization performance.

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Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey