Papers with Dialogue Systems & Conversational Agents
ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer (2023.acl-srw)
Copied to clipboard
| Challenge: | Large-scale language models, such as ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short natural language prompts. |
| Approach: | They conduct a systematic inspection of ChatGPT’s performance in two controllable generation tasks and evaluate the faithfulness of the generated text. |
| Outcome: | The proposed model can adapt output to different target audiences and writing styles, and can generate coherent text with human-authored texts. |
VScript: Controllable Script Generation with Visual Presentation (2022.aacl-demo)
Copied to clipboard
Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske, Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung
| Challenge: | Using a script generation system, scriptwriters can customize their scripts using video retrieval. |
| Approach: | They propose a controllable pipeline that generates complete scripts, including dialogues and scene descriptions, and presents visually using video retrieval. |
| Outcome: | The proposed system outperforms baselines on both automatic and human evaluations, especially in genre control. |
Towards LLM-powered Attentive Listener: A Pragmatic Approach through Quantity Self-Repair (2025.acl-short)
Copied to clipboard
| Challenge: | Quantity Maxims dictates that human speakers aim for optimal quantity of information during conversation. |
| Approach: | They propose to use heuristic path-finding to enable decoder-only LLMs to travel among multiple "Q-alternatives" and search for optimal quantity in coordination with a conversation goal. |
| Outcome: | The proposed techniques are based on heuristic path-finding and can be used to construct human-like, user-centered conversation agents. |
Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond (2023.acl-tutorials)
Copied to clipboard
| Challenge: | Conventional conversation researches focus on the responseability of the system, such as dialogue context understanding and response generation, but overlook the design of an essential property in intelligent conversations, i.e., goal awareness. |
| Approach: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
| Outcome: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
Towards Automated Error Discovery: A Study in Conversational AI (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that LLMs require information about the nature of an error or hints about its occurrence for accurate detection. |
| Approach: | They propose an encoder-based approach to detect and define errors in conversational AI. |
| Outcome: | The proposed framework outperforms baselines across multiple error-annotated dialogue datasets and shows strong generalization to unknown intent detection. |
Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality (2023.eacl-main)
Copied to clipboard
| Challenge: | Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM. |
| Approach: | They propose a topic-aware global-local centrality model to help select the salient context from all sub-topics. |
| Outcome: | The proposed model outperforms baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM. |
Grounding in social media: An approach to building a chit-chat dialogue model (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing open-domain dialogue models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. |
| Approach: | They propose to use social media comments to improve the raw conversation ability of open-domain dialogue systems. |
| Outcome: | The proposed model improves the raw conversation ability of open-domain dialogue systems by mimicking human responses through casual interactions found on social media. |
Spoken Conversational Agents with Large Language Models (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial focuses on the evolution of voice-native LLMs . it reviews the adaptation of text LLM to audio, cross-modal alignment, and joint speech–text training . |
| Approach: | This tutorial examines the evolution of voice-native LLMs in conversational agents . it compares cascaded and voice-based LLM systems to end-to-end retrieval-and vision-grounded systems . |
| Outcome: | This tutorial examines the evolution of voice-native LLMs . it compares the performance of voice assistants to current open-domain agents . |
What we need to learn if we want to do and not just talk (N18-3)
Copied to clipboard
| Challenge: | Existing methods for task-oriented dialogs require fluent natural language responses and correct external actions . but they perform poorly in real world dialog tasks, a new study shows . |
| Approach: | They propose a hybrid model where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. |
| Outcome: | The proposed model achieves a 78% relative improvement in fluency and 200% improvement in accuracy of external calls. |
Full-Duplex-Bench-v2: A Multi-Turn Evaluation Framework for Duplex Dialogue Systems with an Automated Examiner (2026.acl-short)
Copied to clipboard
Guan-Ting Lin, Shih-Yun Shan Kuan, Jiatong Shi, Kai-Wei Chang, Siddhant Arora, Shinji Watanabe, Hung-yi Lee
| Challenge: | Full-duplex speech agents are often half-duplice, alternating turns between user and system. |
| Approach: | They propose a streaming framework that integrates with an examiner that enforces staged goals under two pacing setups. |
| Outcome: | The framework reports fluency, multi-turn instruction following, and task-specific competence. |
EmpathyEar: An Open-source Avatar Multimodal Empathetic Chatbot (2024.acl-demos)
Copied to clipboard
| Challenge: | EmpathyEar is an open-source, avatar-based multimodal empathetic chatbot . currently, ERG systems rely on text, sound, and vision . |
| Approach: | They propose an open-source, avatar-based multimodal empathetic chatbot to fill the gap in traditional text-only ERG systems. |
| Outcome: | The proposed system enables users to generate emotional responses to user queries . it can also generate avatars with talking faces and synchronized speeches . |
Large Scale Multi-Actor Generative Dialog Modeling (2020.acl-main)
Copied to clipboard
| Challenge: | Non-goal oriented dialog agents typically exhibit inconsistent personality across conversations or the average personality of all users. |
| Approach: | They propose a model that conditionally models past conversations to probabilistically model multi-turn conversations in the actor’s persona. |
| Outcome: | The proposed model improves perplexity on 1.7M held out Reddit conversations by 0.47 on scaling from 117M to 8.3B parameters. |
Automatic Dialogue Generation with Expressed Emotions (N18-2)
Copied to clipboard
| 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. |
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog (D19-1)
Copied to clipboard
| Challenge: | Existing methods to learn dialog policy require elaborate design and user goals. |
| Approach: | They propose an algorithm that estimates the reward signal and infers the user goal in dialog sessions. |
| Outcome: | The proposed algorithm achieves higher task success than state-of-the-art models on a multi-domain task-oriented dialog dataset. |
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
Copied to clipboard
| Challenge: | Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality. |
| Approach: | They propose a framework that leverages commonsense-based persona expansion to address such issues in long-term conversations. |
| Outcome: | The proposed framework facilitates better response generation via human-like persona refinement. |
Detecting Response Generation Not Requiring Factual Judgment (2024.naacl-srw)
Copied to clipboard
| Challenge: | Large language models (LLMs) have undergone considerable development and can solve various natural language processing tasks. |
| Approach: | They aimed to achieve both attractiveness and factuality in a dialogue response by crowdsourcing a dataset and performing classification tasks on several models. |
| Outcome: | The proposed model with the highest classification accuracy could yield about 88% accurate classification results. |
Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (2022.acl-srw)
Copied to clipboard
| Challenge: | a new study shows that dialog systems that can hold social talk and make sense of conversational content are not efficient for context-sensitive natural language understanding and reasoning. |
| Approach: | They propose a linguistically-informed architecture to handle social talk in English . they propose linguistic models that fit the context-sensitive components into a Bayesian game-theoretic model . |
| Outcome: | The proposed architecture is based on corpus-based methods but does not track what is happening in a conversation. |
Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee (2022.findings-aacl)
Copied to clipboard
| Challenge: | Existing studies on how people interact with conversational agents have not investigated the interaction authenticity of human-like agents. |
| Approach: | They construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting. |
| Outcome: | The proposed taxonomy can be used for future research and industrial development. |
Hey, wait a minute: on at-issue sensitivity in Language Models (2026.eacl-short)
Copied to clipboard
| Challenge: | Existing methods to evaluate dialogue naturalness are limited. |
| Approach: | They propose a method to assess dialogue naturalness using linguistic notion of at-issueness. |
| Outcome: | The proposed method mitigates bias in linguistic analyses of LMs and tests discourse-sensitive behavior. |
Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation (2021.naacl-srw)
Copied to clipboard
| Challenge: | Existing models for human-like interaction with humans are not expected to improve the accuracy of emotion recognition, but instead focus on generating emotion-aware responses. |
| Approach: | They propose a neural response generation model with multi-task learning of generation and classification, focusing on emotion. |
| Outcome: | The proposed model makes generated responses more emotionally aware. |
Inconsistent dialogue responses and how to recover from them (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to assess and bolster utterance consistency of chat systems have been shown difficult to detect. |
| Approach: | They propose to use annotators to write dialogue responses and recovery utterances to assess and bolster utteration consistency of chat systems. |
| Outcome: | The proposed dataset significantly improves the detection and resolution of inconsistencies in chat conversations. |
GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP (2023.emnlp-main)
Copied to clipboard
| Challenge: | Our study examines ChatGPT’s performance on Arabic languages and dialectal varieties. |
| Approach: | They conduct a large-scale automated and human evaluation of ChatGPT, encompassing 44 distinct language understanding and generation tasks on over 60 different datasets. |
| Outcome: | The proposed model outperforms smaller models on Arabic dialects compared to GPT-4's Modern Standard Arabic and Dialectal Arabic (DA) |
Friend-training: Learning from Models of Different but Related Tasks (2023.eacl-main)
Copied to clipboard
| Challenge: | Current self-training methods focus on improving model performance on a single task. |
| Approach: | They propose a cross-task self-training framework where models trained to do different tasks are used in iterative training, pseudo-labeling, and retraining processes to help each other for better selection of pseudo-labeled labels. |
| Outcome: | The proposed framework achieves the best performance compared to baselines on two dialogue understanding tasks. |
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling (N19-2)
Copied to clipboard
| Challenge: | Existing methods for complex dialog management require limited training data. |
| Approach: | They propose a method for intent recognition for complex dialog management in low resource situations . they use windowed word n-grams, POS tag n grams and pre-trained word embeddings as features . |
| Outcome: | The proposed method performs better with less than 1% of the data size than existing methods but requires considerably more data. |
Dynamic Dialogue Policy for Continual Reinforcement Learning (2022.coling-1)
Copied to clipboard
Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić
| Challenge: | Continual reinforcement learning of the dialogue policy has remained unaddressed . lack of a framework with training protocols, baseline models and suitable metrics has hindered research in this direction. |
| Approach: | They propose a continual learning algorithm, baseline architectures and metrics for assessing continual reinforcement learning models. |
| Outcome: | The proposed architecture can integrate new knowledge seamlessly and achieve significant zero-shot performance when exposed to unseen domains. |
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling (P19-3)
Copied to clipboard
Vighnesh Leonardo Shiv, Chris Quirk, Anshuman Suri, Xiang Gao, Khuram Shahid, Nithya Govindarajan, Yizhe Zhang, Jianfeng Gao, Michel Galley, Chris Brockett, Tulasi Menon, Bill Dolan
| Challenge: | upcoming open-source natural language processing repository aims to train conversational agents for multi-turn situations. |
| Approach: | They present the Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) the framework wraps TensorFlow functionality in a modular component-based architecture. |
| Outcome: | The Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) is an open-source natural language processing repository. |
ASTRO: Automatic Strategy Optimization For Non-Cooperative Dialogues (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for optimizing dialogues require substantial human effort for strategy optimization. |
| Approach: | They propose a fully automated solution that leverages large language models’ self-envolving capabilities to optimize dialogue strategies. |
| Outcome: | The proposed solution significantly improves on baseline models across non-cooperative dialogue tasks, highlighting the potential for autonomously developing such agents without human intervention. |
“Talk to me with left, right, and angles”: Lexical entrainment in spoken Hebrew dialogue (2021.eacl-main)
Copied to clipboard
| Challenge: | entrainment is a widespread phenomenon in human interaction that leads interlocutors to adapt their linguistic productions to become more similar to each other. |
| Approach: | They propose to use existing measures to analyze Hebrew speakers interacting in a Map Task to find evidence of lexical entrainment. |
| Outcome: | The proposed study is the first to examine lexical entrainment in Hebrew using two existing measures. |
MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for emotional support conversation are too coarse-grained to capture user’s instant mental state and focus on expressing empathy in the response rather than gradually reducing user’ s distress. |
| Approach: | They propose a model which firstly infers the user’s fine-grained emotional status and then responds skillfully using a mixture of strategy. |
| Outcome: | The proposed model infers the user’s fine-grained emotional status and responds skillfully using mixed-up strategy modeling. |
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots (2022.findings-acl)
Copied to clipboard
| Challenge: | Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns. |
| Approach: | They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD. |
| Outcome: | The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions . |
Explicit Use of Topicality in Dialogue Response Generation (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing chat dialogue systems only implicitly consider the topic given the context, but not explicitly. |
| Approach: | They propose a dialogue system that responds appropriately following the topic by selecting the entity with the highest “topicality” they define the entity as a noun or compound nouns, and topicality as the degree of speaker awareness directed toward each entity in the dialogue context. |
| Outcome: | The proposed system can follow the topic more than existing systems that only consider the context . |
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets (2023.findings-acl)
Copied to clipboard
Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, Jimmy Huang
| Challenge: | Currently, the evaluation of large language models (LLMs) such as ChatGPT in academic datasets is difficult due to the difficulty of evaluating the generative outputs produced by this model against the ground truth. |
| Approach: | They evaluate ChatGPT across 140 tasks and analyze 255K responses it generates in academic datasets. |
| Outcome: | The proposed model performs well on 140 tasks and generates 255K responses in these datasets. |
Automating Human Evaluation of Dialogue Systems (2022.naacl-srw)
Copied to clipboard
| Challenge: | a recent study shows that human evaluations of dialogue systems weakly reflect human judgments. |
| Approach: | They propose a BERT-based model that fine-tunes a model with three prediction heads to predict whether the system-generated output is natural, fluent, and informative. |
| Outcome: | The proposed model achieves an average accuracy of 77% over the 3 labels . it also uses three different models to compute the labels compared to three separate models . |
Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Modern dialog managers face the challenge of having to fulfill human-level conversational skills as part of common user expectations. |
| Approach: | They propose to extract the intent argument of non-canonical directives in a natural language format and build a parallel corpus for this purpose. |
| Outcome: | The proposed method extracts the intent argument of non-canonical directives in a natural language format, which may yield more accurate parsing. |
Is a Knowledge-based Response Engaging?: An Analysis on Knowledge-Grounded Dialogue with Information Source Annotation (2023.acl-srw)
Copied to clipboard
| Challenge: | Currently, most knowledge-grounded dialogue models focus on reflecting given external knowledge. |
| Approach: | They analyze human behavior by annotating utterances in an existing knowledge-grounded dialogue corpus and find that speaker-derived information improves dialogue engagingness. |
| Outcome: | The proposed model cannot include speaker-derived information as often as humans do. |
Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) can be adopted as tutoring agents for math and language learning. |
| Approach: | They propose a framework to construct profiles of different student groups by refining and integrating both cognitive and noncognitive aspects, and leverage LLMs for personality-aware student simulation in a language learning scenario. |
| Outcome: | The proposed framework can construct profiles of different student groups by refining and integrating both cognitive and noncognitive aspects, and leverage LLMs for personality-aware student simulation in a language learning scenario. |
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)
Copied to clipboard
| Challenge: | Document-grounded dialogs need smooth transitions between knowledge selected for generating responses. |
| Approach: | They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic. |
| Outcome: | The proposed method outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. |
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (2022.coling-1)
Copied to clipboard
| 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. |
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation (2021.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to building task-oriented dialog systems require a substantial amount of annotations and thus are labor-intensive. |
| Approach: | They propose a Pre-trainedRole Alternating Language model (PRAL) that is explicitly designed for task-oriented dialog tasks. |
| Outcome: | The proposed model outperforms or is on par with state-of-the-art models on task-oriented dialog tasks. |
To Chat or Task: a Multi-turn Dialogue Generation Framework for Task-Oriented Dialogue Systems (2025.acl-industry)
Copied to clipboard
| Challenge: | Large language models (LLMs) are designed to handle complex task requests, but lack of specific datasets for training and evaluation of such systems . |
| Approach: | They propose a framework to generate a dataset for in-vehicle speech recognition systems . they train an in-car context sensor that correctly identifies the functional intent of the driver . |
| Outcome: | The proposed framework outperforms baseline models across experimental settings. |
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations (2022.coling-1)
Copied to clipboard
| Challenge: | Existing metrics to measure the performance of conversational AI assistants are difficult to establish due to their slow nature. |
| Approach: | They propose an automatic dialogue evaluation framework that performs goal segmentation and success prediction by adding multi-task learning heads. |
| Outcome: | The proposed model achieves on-par with human annotation compared to a gold annotation benchmark. |
(CPER) From Guessing to Asking: An Approach to Resolving Persona Knowledge Gap in LLMs during Multi-Turn Conversations (2025.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods for identifying and resolving persona knowledge gaps are underexplored. |
| Approach: | They propose a framework that dynamically detects and resolves persona knowledge gaps using intrinsic uncertainty quantification and feedback-driven refinement. |
| Outcome: | The proposed framework detects and resolves persona knowledge gaps using intrinsic uncertainty quantification and feedback-driven refinement on two real-world datasets: CCPE-M for preferential movie recommendations and ESConv for mental health support. |
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues (2024.findings-eacl)
Copied to clipboard
| Challenge: | blending multiple languages within a single conversation presents a formidable challenge, given the wide-ranging variations influenced by individual speaking styles and cultural backgrounds. |
| Approach: | They propose a novel approach to harness the Big Five personality traits acquired in an unsupervised manner from code-mixed conversations to bolster the performance of response generation. |
| Outcome: | The proposed approach enhances contextual relevance and performance of the proposed model by combining personality traits with dialogue context. |
AMAN: Agent for Mentoring and Assisting Newbies in MMORPG (2025.coling-industry)
Copied to clipboard
| Challenge: | AMAN is a chatbot designed to help novice gamers learn the gameplay mechanics of online games. |
| Approach: | They propose a model that functions as a human-like chat buddy that interacts with users in a friendly manner while providing substantive informational depth. |
| Outcome: | The proposed model integrates continual pre-training with a sequence of online resources and instruction tuning on curated dialogues. |
LEEETs-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems (2024.findings-naacl)
Copied to clipboard
| Challenge: | Linguistic entrainment is a phenomenon where linguistic patterns employed by conversational participants converge to one another. |
| Approach: | They propose methods for achieving dialogue entrainment in a task-oriented dialogue system using shared vocabulary. |
| Outcome: | The proposed model produces significantly better entrainment than the base model. |
Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agents (2026.eacl-industry)
Copied to clipboard
| Challenge: | Multi-turn tool-calling models have emerged as a key feature in modern AI assistants, but their success in safety-critical industries remains constrained by concerns about transparency and model resilience. |
| Approach: | They propose a new evaluation paradigm for multi-turn function-calling LLMs that provides holistic metrics that evaluate a model’s behavior when confronted with misleading assertions. |
| Outcome: | The proposed evaluation paradigm evaluates a model's behavior when confronted with misleading assertions originating from two distinct vectors: (1) user-sourced assertions (USAs) and (2) function-sourced assertions (FSAs). |
Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration (2022.tacl-1)
Copied to clipboard
| Challenge: | Neural generative open-domain english-language dialogue agents are currently unsuitable for applications other than entertainement and research. |
| Approach: | They propose to incorporate metacognitive features into the training of a controllable generation model to improve likelihood of correctness. |
| Outcome: | The proposed model improves likelihood of correctness by incorporating metacognitive features into the training of a controllable generation model. |
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (P19-1)
Copied to clipboard
| Challenge: | Emotion recognition in conversations has gained popularity due to its potential applications. Until now, a large multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. |
| Approach: | They propose to extend and enhance EmotionLines by combining 13,000 utterances from Friends dialogues with emotion and sentiment labels. |
| Outcome: | The proposed dataset contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. |
Construction Repetition Reduces Information Rate in Dialogue (2022.aacl-main)
Copied to clipboard
| Challenge: | We observe that construction usage lowers the information content of utterances. |
| Approach: | They propose to use construction repetition to mitigate information rate in English open-domain spoken dialogues. |
| Outcome: | The proposed method lowers the information content of utterances, while increasing the frequency and density of repetition. |
Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach (P18-1)
Copied to clipboard
| Challenge: | Existing coherence models are not able to distinguish coherent discourses from incoherent ones. |
| Approach: | They propose a novel coherence model for written asynchronous conversations . they propose to lexicalize the model's entity transitions and extend it to asynchron conversations based on conversational structure . |
| Outcome: | The proposed model outperforms existing models on coherence assessment and thread reconstruction tasks. |
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing research focuses on task-oriented or open-domain dialogue systems with influence skills. |
| Approach: | They propose to define and introduce a category of social influence dialogue systems that influence users’ cognitive and emotional responses. |
| Outcome: | The proposed system is task-oriented or goal-oriented, but it is not open-domain. |
From Detection to Explanation: Modeling Fine-Grained Emotional Social Influence Techniques with LLMs and Human Preferences (2026.eacl-srw)
Copied to clipboard
Maciej Markiewicz, Wiktoria Mieleszczenko-Kowszewicz, Beata Bajcar, Tomasz Adamczyk, Aleksander Szczęsny, Jolanta Babiak, Przemyslaw Kazienko
| Challenge: | Using Large Language Models (LLMs) is becoming increasingly important in communication and persuasion. |
| Approach: | They investigate the capabilities of Large Language Models (LLMs) to detect and explain emotional social influence techniques in textual dialogues. |
| Outcome: | The proposed models perform poorly on two tasks: detecting emotional social influence techniques and identifying text spans corresponding to specific techniques. |
Long-term Control for Dialogue Generation: Methods and Evaluation (2022.naacl-main)
Copied to clipboard
| 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. |
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 (2020.acl-main)
Copied to clipboard
| Challenge: | End-to-end dialogue systems with monolithic neural architecture are often trained with input-output utterances without taking into account the entire annotations available in the corpus. |
| Approach: | They propose an end-to-end neural architecture for goal-oriented dialogue systems that addresses both challenges . they propose a modular architecture where modules are optimized individually . |
| Outcome: | The proposed system achieved the top position in the human evaluation task . it is based on a neural architecture that can be integrated with external systems . |
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)
Copied to clipboard
| 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. |
Negation Detection in Dutch Spoken Human-Computer Conversations (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing negation detection methods in English are not available. |
| Approach: | They propose to annotate a Dutch dialogue corpus with negation cues and their scopes. |
| Outcome: | The proposed method can detect negation cues and scope in Dutch dialogues with high precision and recall. |
Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)
Copied to clipboard
Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He
| Challenge: | Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state. |
| Approach: | They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states . |
| Outcome: | The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets. |
Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Empathy is a multi-dimensional concept consisting of cognitive and affective aspects. |
| Approach: | They propose two new in-context example selection methods that utilize emotion and situational information. |
| Outcome: | The proposed method is effective in measuring the degree of human empathy. |
Learning Neuro-Symbolic World Models with Conversational Proprioception (2023.acl-short)
Copied to clipboard
| Challenge: | Existing neuro-symbolic approaches to natural language-based interactions are model-free, but there is a need for model-based approaches. |
| Approach: | They propose a model-free approach to learning a logical policy in a text-based game . they use a neural network to enhance the internal logic state with a memory of previous actions . |
| Outcome: | The proposed method can learn neuro-symbolic world models on the TextWorld-Commonsense set of games. |
A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)
Copied to clipboard
Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu
| Challenge: | a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes . |
| Approach: | They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. |
| Outcome: | The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure. |
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Knowledge-based open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. |
| Approach: | They propose a benchmark for evaluating multi-source dialogue knowledge selection and response generation using Wikipedia's wizard of Wikipedia. |
| Outcome: | The proposed benchmark is called multi-source Wizard of Wikipedia (Ms.WoW) it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. |
Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)
Copied to clipboard
| Challenge: | Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios. |
| Approach: | They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents. |
| Outcome: | The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally. |
Novel Feature Discovery for Task-Oriented Dialog Systems (2023.findings-eacl)
Copied to clipboard
| Challenge: | Prior work on novelty detection limits the scope of features represented by novel single intents to those represented by multiple user-perceived fine-grained features belonging to the same intent. |
| Approach: | They propose to use a feature discovery technique to discover novel features from user utterances rather than single intent discovery to classify them into slots. |
| Outcome: | The proposed approach consistently detects novel features from user utterances on two datasets. |
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark (2022.tacl-1)
Copied to clipboard
| Challenge: | Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. |
| Approach: | They propose to evaluate the validity of 12k dialogue turns generated by neural dialogue systems trained on three knowledge-grounded dialogue corpora and to use them to analyze eight evaluation metrics. |
| Outcome: | The proposed evaluation metrics rely on spurious correlations, do not reliably distinguish attributable abstractive responses from unattributable ones, and perform substantially worse when the knowledge source is longer. |
Utterance-level Detection Framework for LLM-Involved Content Detection in Conversational Setting (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing methods focus on static, document-level content, overlooking the dynamic nature of dialogues. |
| Approach: | They propose an utterance-level detection framework which integrates features from individual and combined analysis of dialogue participants’ responses to detect LLM-generated text under conversational setting. |
| Outcome: | The proposed framework achieves 98.14% accuracy with high inference speed and extensive results on different models and settings. |
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Standard language generation metrics have been shown to be ineffective for dialog evaluation. |
| Approach: | They propose an unsupervised evaluation metric for dialog that trains unsupervised models to measure several desirable qualities of dialog. |
| Outcome: | The proposed evaluation metric strongly correlates with human judgment on Topical-Chat and PersonaChat. |
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset (2024.findings-acl)
Copied to clipboard
Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee
| Challenge: | Existing datasets for conversational recommender systems lack specific user preferences and explanations for recommendations . current datasets lack specific preferences, hindering high-quality recommendations despite advances in large language models . |
| Approach: | They propose to synthesize a conversational recommendation dataset with persona- and knowledge-augmented LLM simulators to address these challenges. |
| Outcome: | The proposed dataset outperforms baselines in human and automatic evaluations. |
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for improving consistency often train with additional NLI labels or attach trained extra modules to the generative agent. |
| Approach: | They propose to encode personas into dialogue embeddings and a persona-conditioned dialogue dataset to improve persona consistency. |
| Outcome: | The proposed approach can enforce dialogue agents to refrain from contradictions and improve consistency of existing models. |
UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue (2022.acl-short)
Copied to clipboard
| Challenge: | Existing studies tackle the problem of error propagation by decomposing the goal-oriented document-grounded dialogue into two sub-tasks. |
| Approach: | They propose to unify knowledge identification and response generation into two sub-tasks by sequentially generating grounding knowledge and response. |
| Outcome: | The proposed framework unifies knowledge identification and response generation and models their characteristics using a prompt-connected multi-task learning strategy. |
LLM ContextBridge: A Hybrid Approach for Intent and Dialogue Understanding in IVSR (2025.coling-industry)
Copied to clipboard
| Challenge: | In-vehicle speech recognition systems struggle with interpreting user intent accurately due to limitations in contextual understanding and ambiguity resolution. |
| Approach: | They propose a hybrid architecture that integrates Pretrained Language Model-based intent classification with Large Language Models to enhance both command recognition and dialogue management. |
| Outcome: | The proposed architecture improves recognition accuracy and user experience in multi-turn dialogues. |
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness (2020.emnlp-main)
Copied to clipboard
| 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. |
Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality. |
| Approach: | They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality. |
| Outcome: | The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy. |
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | a context leads to various responses, and a response answers multiple contexts. |
| Approach: | They propose a method that augments open-domain dialogue generation from a many-to-many perspective. |
| Outcome: | The proposed method can augment open-domain dialogue generation tasks with automatic and human evaluation. |
A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models (2020.lrec-1)
Copied to clipboard
| Challenge: | Fully data driven Chatbots suffer from inconsistent behaviour across their turns due to a general difficulty in controlling parameters like their assumed background personality and knowledge of facts. |
| Approach: | They propose a model that is based on pre-specified facts and opinions and validates the dialogues for adherence to their given fact and opinion profile. |
| Outcome: | The proposed model is able to generate opinionated responses that are judged to be natural and knowledgeable and show attentiveness. |
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (D18-1)
Copied to clipboard
| Challenge: | Existing dialog datasets rely on human labeling, which is expensive, limited in size, and in low coverage. |
| Approach: | They propose a framework to automatically cluster dialogue intents and slots . they collect context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. |
| Outcome: | The proposed framework can promote human labeling cost to a great extent and achieve good intent clustering accuracy (84.1%) it also provides reasonable and instructive slot labeling results. |
Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems (2020.findings-emnlp)
Copied to clipboard
| Challenge: | In order to improve the sample-efficiency of deep reinforcement learning, we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS). |
| Approach: | They propose to use an actor-double-critic to improve the stability and overall performance of imagination augmented agent (I2A) in spoken dialogue systems. |
| Outcome: | The proposed model-based agent (ADC) improves the stability and sample-efficiency of deep reinforcement learning (DRL) on a restaurant booking task. |
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (D18-1)
Copied to clipboard
| 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. |
Persona or Context? Towards Building Context adaptive Personalized Persuasive Virtual Sales Assistant (2022.aacl-main)
Copied to clipboard
Abhisek Tiwari, Sriparna Saha, Shubhashis Sengupta, Anutosh Maitra, Roshni Ramnani, Pushpak Bhattacharyya
| Challenge: | Existing task-oriented conversational agents assume that end-users will always have a pre-determined and servable task goal, which results in dialogue failure in hostile scenarios, such as goal unavailability. |
| Approach: | They propose to build an end-to-end multi-modal persuasive dialogue system incorporating a personalized persuasive module aided goal controller and goal persuader. |
| Outcome: | The proposed system achieves user tasks even in goal unavailability scenarios by persuading them towards a similar and servable goal. |
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators (2020.lrec-1)
Copied to clipboard
| Challenge: | Emotion recognition helps to build natural dialogue systems. |
| Approach: | They propose to use a recurrent neural model to annotate emotion corpora with dialogue act labels and an ensemble annotator to extract the final dialogue act label. |
| Outcome: | The proposed model annotates two accessible multi-modal emotion corpora with and without context and extracts the final dialogue act label. |
PACO: a Corpus to Analyze the Impact of Common Ground in Spontaneous Face-to-Face Interaction (2020.lrec-1)
Copied to clipboard
| Challenge: | PAC0 is a conversational corpus of 15 face-to-face interactions lasting around 20 min each. |
| Approach: | They have created a conversational corpus of 15 face-to-face dyadic interactions lasting around 20 min each. |
| Outcome: | The compared corpus consists of 15 face-to-face dyadic interactions lasting around 20 min each. |
Dialogue Act Annotation in a Multimodal Corpus of First Encounter Dialogues (2020.lrec-1)
Copied to clipboard
| Challenge: | a method used to annotate dialogue acts in a multimodal corpus is described . the annotations allow for analysis of how multimodal signals contribute to the structure and content of the dialogues. |
| Approach: | They propose to annotate dialogue acts in a multimodal corpus of first encounter dialogues . they focus on which dialogue acts often follow each other across speakers and which overlap gestural behaviour . |
| Outcome: | The method used to annotate dialogue acts in a multimodal corpus is described. |
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information (D18-1)
Copied to clipboard
| Challenge: | Existing work on how to generate relevant and informative responses is focusing on how dialogue systems generate information from large dialogue corpus. |
| Approach: | They propose to use dialogue corpus to generate relevant responses by using prototypes to extract semantic information from PMN. |
| Outcome: | The proposed model outperforms classical and strong baseline models in generating relevant and informative responses. |
Designing Multilingual Interactive Agents using Small Dialogue Corpora (2020.lrec-1)
Copied to clipboard
| 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. |
Multimodal Corpus of Bidirectional Conversation of Human-human and Human-robot Interaction during fMRI Scanning (2020.lrec-1)
Copied to clipboard
Birgit Rauchbauer, Youssef Hmamouche, Brigitte Bigi, Laurent Prévot, Magalie Ochs, Thierry Chaminade
| Challenge: | a study of real-life bi-directional conversations combines multimodal corpus with neural, physiological and behavioral data. |
| Approach: | They propose a multimodal corpus derived from natural conversations . they used human-human interactions as a control condition . |
| Outcome: | The proposed corpus includes neural, physiological and behavioral data. |
Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use. |
| Approach: | They propose a benchmark to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues. |
| Outcome: | The proposed benchmark covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior. |
CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems (2024.findings-acl)
Copied to clipboard
Abbas Ghaddar, David Alfonso-Hermelo, Philippe Langlais, Mehdi Rezagholizadeh, Boxing Chen, Prasanna Parthasarathi
| Challenge: | CHARP is a testbed for knowledge-grounded dialogue evaluation of models trained on FaithDial data. |
| Approach: | They propose a testbed for evaluating models trained on FaithDial with annotation artifacts that may bias models towards completely ignoring the conversation history. |
| Outcome: | The proposed model fails to accurately evaluate the conversational history and lacks hallucination detection. |
MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing contextual safety benchmarks are mostly single-turn and miss how malicious intent can emerge gradually or how the same scene can support both benign and exploitative goals. |
| Approach: | They propose a benchmark that evaluates contextual safety in multimodal large language models . they observe persistent trade-offs between contextual safety and utility . |
| Outcome: | The proposed model combines multi-turn and multi-switch scenarios to evaluate safety in multimodal large language models. |
Weakly supervised discourse segmentation for multiparty oral conversations (2021.emnlp-main)
Copied to clipboard
| Challenge: | Discourse segmentation is the first step of discourse analysis. |
| Approach: | They propose a weak supervision approach to adapt a latent model to French conversation transcripts with a linguistic and acoustic input. |
| Outcome: | The proposed model improves in situations where speaker turns are lacking or noisy, gaining up to 13% in F-score. |
Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for identifying suicidal ideation in phone conversations are difficult to use because of their long duration and noisy nature. |
| Approach: | They propose a self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. |
| Outcome: | The proposed approach outperforms the baseline models in overall performance with an F score of 66.01% and significantly higher F-score in detecting the most dangerous cases. |
A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation (2025.coling-main)
Copied to clipboard
Ekai Hashimoto, Mikio Nakano, Takayoshi Sakurai, Shun Shiramatsu, Toshitake Komazaki, Shiho Tsuchiya
| Challenge: | a slot-filling-based interview dialogue system is limited in the flexibility of information collection . authors propose a method that leverages large language models to generate new slots according to the flow of the dialogue . |
| Approach: | They propose a slot-filling dialogue system that collects information on staff careers . they incorporate abduction into the slot generation process to enable more natural conversations . |
| Outcome: | The proposed method improves the efficiency and quality of career interviews conducted by nursing managers. |
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset (2024.naacl-long)
Copied to clipboard
| 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. |
GrounDial: Human-norm Grounded Safe Dialog Response Generation (2024.findings-eacl)
Copied to clipboard
| Challenge: | Recent conversational AI systems generate unsafe responses agreeing to offensive user input or including toxic content. |
| Approach: | They propose a method where response safety is achieved by grounding responses to commonsense social rules without fine-tuning. |
| Outcome: | The proposed approach is quantitatively and qualitatively safer even without additional data or tuning. |
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation (C18-1)
Copied to clipboard
| Challenge: | Neural encoder-decoder models tend to generate meaningless and generic responses regardless of what the input text is. |
| Approach: | They propose an easy-to-extend learning framework based on latent vectors to provide training guidance without resorting to extra data or complicating network’s inner structure. |
| Outcome: | The proposed framework improves the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies. |
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking (2021.acl-short)
Copied to clipboard
| Challenge: | Existing dialog state tracking models neglect rich structural information in a dataset. |
| Approach: | They propose to use curriculum learning to leverage dialog state tracking data . they propose a model-agnostic framework that pre-trains a DST model with schema information . |
| Outcome: | The proposed framework improves performance over a transformer-based and RNN-based model on WOZ2.0 and MultiWOZ2.1. |
Audiobook Dialogues as Training Data for Conversational Style Synthetic Voices (2022.lrec-1)
Copied to clipboard
Liisi Piits, Hille Pajupuu, Heete Sahkai, Rene Altrov, Liis Ermus, Kairi Tamuri, Indrek Hein, Meelis Mihkla, Indrek Kiissel, Egert Männisalu, Kristjan Suluste, Jaan Pajupuu
| Challenge: | Synthetic voices are increasingly used in applications that require a conversational speaking style. |
| Approach: | They compare voices trained on audiobook character speech corpus, audiobook narrator speech corpu and neutral-style sentence-based corpus . they conclude that the character speech and neutral style corpus are more suitable . |
| Outcome: | The evaluation of voices trained on three corpora of equal size was conducted by voice chatbots . the results may have been confounded by the greater acoustic variability and poorer phonemic coverage . |
Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)
Copied to clipboard
| 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. |
QualEval: Qualitative Evaluation for Model Improvement (2024.naacl-long)
Copied to clipboard
Vishvak Murahari, Ameet Deshpande, Peter Clark, Tanmay Rajpurohit, Ashish Sabharwal, Karthik Narasimhan, Ashwin Kalyan
| Challenge: | Quantitative evaluation metrics are inadequate for large language models due to complexity of tasks and cannot provide actionable diagnostics. |
| Approach: | They propose a quantitative evaluation tool called QualEval that uses automated qualitative evaluation as a vehicle for model improvement. |
| Outcome: | The proposed method improves the performance of the Llama 2 model by 15% compared to baselines. |
Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees’ Dialogue to Facilitate Nurse Communication Training (2025.findings-acl)
Copied to clipboard
Keyeun Lee, Seolhee Lee, Esther Hehsun Kim, Yena Ko, Jinsu Eun, Dahee Kim, Hyewon Cho, Haiyi Zhu, Robert E. Kraut, Eunyoung E. Suh, Eun-mee Kim, Hajin Lim
| Challenge: | standardized patient (SP) simulations are costly and inflexible. |
| Approach: | They propose a framework that leverages large language models to dynamically adapt VP behavior based on trainee input. |
| Outcome: | The proposed framework reflects real-world communication skills and produces more natural and realistic interactions than existing methods. |
Be Helpful but Don’t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support (2024.emnlp-main)
Copied to clipboard
| Challenge: | a helpful speaker should maintain an "effect-effort" tradeoff for a conversation to help and support . a study aimed to cultivate the awareness of "optimal relevance" into the cognitive process of conversation agents . |
| Approach: | They integrate the "Cognitive Relevance Principle" into emotional support agents . they found that the "relevance principle" is effective in generating human-like, helpful, harmless conversations . |
| Outcome: | The proposed method improves human-likedness and support in multi-turn conversations . the source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git . |
Exemplar Encoder-Decoder for Neural Conversation Generation (P18-1)
Copied to clipboard
| Challenge: | Existing approaches to generate conversational systems suffer from lack of diversity in responses and generation of short, repetitive and uninteresting responses. |
| Approach: | They propose a novel conversation model that uses similar examples from training data to generate responses. |
| Outcome: | The proposed model outperforms state-of-the-art sequence to sequence learning on several evaluation metrics on two large data sets. |
KTH Tangrams: A Dataset for Research on Alignment and Conceptual Pacts in Task-Oriented Dialogue (L18-1)
Copied to clipboard
| Challenge: | Existing studies on instructor-manipulator dialogue use disparate but similar datasets . a recent study examined the alignment of referring expressions (RL) in situated dialogue . |
| Approach: | They propose to use a corpus of referring expressions in a relatively free dialogue with physical features generated in simulated situations to study alignment in referring language. |
| Outcome: | The proposed datasets facilitate analysis of dialogic linguistic phenomena regarding alignment in the formation of referring expressions known as conceptual pacts. |
Adding Chit-Chat to Enhance Task-Oriented Dialogues (2021.naacl-main)
Copied to clipboard
Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
| Challenge: | Existing dialogue systems focus on functional goals, open-domain chatbots on socially engaging conversations. |
| Approach: | They propose to add chit-chat to ENhance Task-ORiented dialogues by a human-assisted data collection approach to augment task-oriented dialogues with minimal annotation effort. |
| Outcome: | The proposed models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike while maintaining competitive task performance. |
Exploring the Role of Context in Utterance-level Emotion, Act and Intent Classification in Conversations: An Empirical Study (2021.findings-acl)
Copied to clipboard
| Challenge: | utterance-level dialogue understanding tasks are often performed at utterrance level and are often conjoined together under the umbrella of utterence-level dialog understanding. |
| Approach: | They propose to use a contextual utterance-level dialogue understanding baseline as a strong framework for six dialogue-understanding tasks. |
| Outcome: | The proposed framework can be easily adapted for other tasks for similar purposes. |
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents (2024.findings-acl)
Copied to clipboard
Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Gao Xing, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang
| Challenge: | Existing studies on role-playing agents have focused on enhancing their conversational capability, role-specific knowledge and style, but there has been a gap in assessing their social intelligence. |
| Approach: | They propose a benchmark to evaluate the sociality of role-playing agents using LLMs. |
| Outcome: | The proposed benchmark is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances. |
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (P18-1)
Copied to clipboard
Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain
| Challenge: | Prior work focused on characterizing and detecting content exhibiting antisocial online behavior. |
| Approach: | They propose a task of predicting from the very start of a conversation whether it will get out of hand. |
| Outcome: | The proposed framework can detect early warning signs of antisocial behavior in online conversations. |
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Entity name matching always retrieves irrelevant facts from the view of local entity words. |
| Approach: | They propose a knowledge selection approach and a generative model that integrates commonsense knowledge into the dialogue response generation by integrating commonsensical knowledge into a query. |
| Outcome: | The proposed approach improves on the most metrics and comparable to baselines. |
Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Documents contain various structures that hinder the ability of machines to comprehend . user information needs are often underspecified, and the nature of heterogeneous documents poses challenges. |
| Approach: | They propose a dataset for building machines that help users seek information via conversations . their dataset contains over 100,000 turns based on Chinese documents from five domains . |
| Outcome: | The proposed tasks are challenging and worthy of further research. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
Copied to clipboard
Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
A Unified Approach to Entity-Centric Context Tracking in Social Conversations (2022.lrec-1)
Copied to clipboard
| Challenge: | Context Tracking is a computational task for human-human conversations . it involves identifying important entities and keeping track of their properties and relationships . |
| Approach: | They propose to use a human-human conversation corpus for context tracking with people and location annotations to model the conversation's context. |
| Outcome: | The proposed model is based on a large human-human conversation corpus with people and location annotations. |
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue (2024.eacl-long)
Copied to clipboard
| Challenge: | Knowledge-grounded dialogues require a balance between being specific to what the conversation partner has said and being attributable to an underlying source document. |
| Approach: | They propose a framework that allows to experiment with various plan variables supported by prior work . they show that metric-aware planning mechanisms are better at automatic evaluations but underperform in human judgment compared to metric agnostic mechanisms. |
| Outcome: | The proposed framework supports metric-agnostic and metric aware content planning, but it underperforms in human judgment. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
Modeling Dialogue in Conversational Cognitive Health Screening Interviews (2020.lrec-1)
Copied to clipboard
| Challenge: | Dementia is one of the most pressing healthcare concerns as median age rises . a conversational agent capable of conducting cognitive health screening interviews could be an inexpensive, flexible, low-stress alternative . |
| Approach: | They propose an annotation schema for assigning dialogue act labels to utterances in patient-interviewer conversations collected as part of a clinically-validated cognitive health screening task. |
| Outcome: | The proposed system is characterized by high inter-annotator agreement and is able to perform clinically-validated cognitive health screening tasks. |
Cross Copy Network for Dialogue Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Despite the success of sequence-to-sequence models, dialogue logics are often ignored. |
| Approach: | They propose a network architecture to explore the current dialog context and similar dialogue instances’ logical structure simultaneously. |
| Outcome: | The proposed network architecture is superior to existing state-of-the-art models. |
Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
| Approach: | They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on DSTC7 and DSTF8* with competitive results on UbuntuV2 . |
A Framework for Exploring Player Perceptions of LLM-Generated Dialogue in Commercial Video Games (2023.findings-emnlp)
Copied to clipboard
| Challenge: | evaluating the player experience in a roleplaying game augmented with LLM-generated dialogue remains a major challenge. |
| Approach: | They propose a dynamic evaluation framework for the dialogue management systems that govern the task-oriented dialogue often found in roleplaying video games. |
| Outcome: | The proposed framework directly evaluates the performance of LLM-generated dialogue in a role-playing game with 28 players. |
ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
Copied to clipboard
Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase. |
| Approach: | They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift. |
| Outcome: | The proposed model outperforms baselines while reducing token consumption. |
Gaussian Process based Deep Dyna-Q approach for Dialogue Policy Learning (2021.findings-acl)
Copied to clipboard
| Challenge: | Reinforcement learning (RL) is the main dialogue policy learning method in recent years. |
| Approach: | They propose a Gaussian Process based Deep Dyna-Q approach to dialogue policy learning . they propose evaluating the quality of experiences generated by the world model using a discriminator . |
| Outcome: | The proposed approach improves the effectiveness and efficiency of dialogue policy learning by 20% with fewer human-machine interactions. |
Personalized Response Generation via Generative Split Memory Network (2021.naacl-main)
Copied to clipboard
| Challenge: | Despite the success of text generation and dialogue systems, how to endow a text generation system with personality traits remains under-investigated. |
| Approach: | They propose a model to generate personalized responses on reddit using user profiles and posting histories. |
| Outcome: | The proposed model improves over the state-of-the-art response generation models. |
Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing alignment methods focus on universal human values or static, single-turn preferences, thereby failing to address the critical needs of long-term personalization and the initial user cold-start problem. |
| Approach: | They propose a user-centric lifelong agent that continuously infers and adapts to user preferences. |
| Outcome: | The proposed agent achieves superior performance over strong prompt-based and policy optimization baselines, not only in idealized but also in noisy conversational contexts. |
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
Copied to clipboard
| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
| Outcome: | The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus. |
Reference-Centric Models for Grounded Collaborative Dialogue (2021.emnlp-main)
Copied to clipboard
| Challenge: | Using a structured referent grounding module, we can effectively ground and inform a partner's utterances to their own context. |
| Approach: | They propose a grounded neural dialogue model that works with people in a partially-observable reference game. |
| Outcome: | The proposed model outperforms state-of-the-art models on a spatial grounding dialogue task and achieves a 20% relative improvement in human evaluations. |
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues (2020.lrec-1)
Copied to clipboard
| Challenge: | Using the MPAA rating, movie content can negatively affect children’s behaviour, for example, watching specific programs may encourage irresponsible sexual behavior and alcohol usage in teenagers. |
| Approach: | They propose an RNN-based architecture that jointly models the genre and the emotions in the script to predict the MPAA rating. |
| Outcome: | The proposed model outperforms the traditional machine learning method by 7% and achieves an 81% weighted F1 score. |
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing word-level policy models that learn dialog policy and language generation from dialog corpora often lead to degeneration issues where the utterances become ungrammatical or repetitive. |
| Approach: | They propose to represent prior dialog transitions as a graph and learn a CG grounded dialog policy that can foster a more coherent and controllable dialog. |
| Outcome: | The proposed framework is able to learn dialog policy in open-domain multi-turn conversation. |
An Efficient Task-Oriented Dialogue Policy: Evolutionary Reinforcement Learning Injected by Elite Individuals (2025.acl-long)
Copied to clipboard
| Challenge: | Evolutionary Algorithms (EAs) have been proven to effectively explore the solution space of neural networks by maintaining population diversity. |
| Approach: | They propose an elite individual injection mechanism to enhance EA’s search efficiency by adaptively introducing best-performing individuals into the population. |
| Outcome: | Experiments on four datasets show that the proposed approach significantly improves the balance between exploration and exploitation, boosting performance. |
Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on dialogue modeling use pre-trained language models to encode dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. |
| Approach: | They propose a bidirectional information decoupling network as a universal dialogue encoder which explicitly incorporates both the past and future contexts. |
| Outcome: | The proposed model incorporates past and future contexts and can be generalized to a wide range of dialogue-related tasks. |
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity (2022.findings-naacl)
Copied to clipboard
| Challenge: | State-of-the-art open-domain dialogue models fail to maintain character identity throughout discourse . despite improvements in accuracy and self-contradiction, agents take on the role of interlocutor . |
| Approach: | They formalize and quantify the deficiency in character identity modeling by using human evaluations. |
| Outcome: | The proposed models reduce mistaken identity issues by nearly 65% according to human annotators while improving engagingness. |
Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
Copied to clipboard
| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (N18-1)
Copied to clipboard
| Challenge: | Existing methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. |
| Approach: | They propose a hybrid imitation and reinforcement learning method that integrates user feedback and reinforcement training to improve the agent's performance. |
| Outcome: | The proposed method can learn from the mistake it makes via imitation learning from user teaching and feedback. |
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to generate pre-trained language models with attributes are expensive and overfitted on small training sets. |
| Approach: | They propose a novel approach to control the generation of Transformer-based pre-trained language models using a new control attributes loss framework. |
| Outcome: | The proposed method is shown to perform well with very limited training samples. |
OTTers: One-turn Topic Transitions for Open-Domain Dialogue (2021.acl-long)
Copied to clipboard
| Challenge: | a mixed-initiative dialogue system is often purely responsive, make abrupt transitions, or fail to take initiative. |
| Approach: | They propose a task to generate a "bridging" utterance connecting a new topic to the previous conversation turn. |
| Outcome: | The proposed task generates a "bridging" utterance connecting a new topic to the previous topic. |
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)
Copied to clipboard
| Challenge: | generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging. |
| Approach: | They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator. |
| Outcome: | The proposed framework outperforms baseline models in a variety of experiments. |
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to dialogue state tracking rely on pre-defined ontologies . however, these methods suffer from computational complexity that increases proportionally to the number of pre-determined slots. |
| Approach: | They propose a model that generates a sequence of belief states without the pre-defined ontology list. |
| Outcome: | The proposed model scales easily with the increasing number of pre-defined slots and domains and reaches the state-of-the-art performance on the multi-domain and single domain dialogue state tracking datasets. |
Curricular Next Conversation Prediction Pretraining for Transcript Segmentation (2023.findings-eacl)
Copied to clipboard
| Challenge: | Prior research on document segmentation has focused on segmenting documents such as Wikipedia articles. |
| Approach: | They propose to pretrain a model to identify consecutive conversations to address these challenges . they introduce a curriculum to Advanced NCP to make the task more relevant to the downstream task . |
| Outcome: | The proposed model outperforms previous models in speech recognition errors and is robust to speech recognition. |
A Survey on LLM-based Conversational User Simulation (2026.eacl-long)
Copied to clipboard
Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi
| Challenge: | Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation. |
| Approach: | They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments . |
| Outcome: | The proposed model enables high-fidelity generation of synthetic user conversation. |
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
Copied to clipboard
| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
Chat-Ghosting: Methods for Auto-Completion in Dialog Systems (2026.eacl-long)
Copied to clipboard
| Challenge: | Ghosting is a type-ahead completion task that predicts a user's intended input for inline query auto-completion (QAC). |
| Approach: | They propose to use ghosting to predict a user's intended input for inline query auto-completion by suggesting completions to incomplete queries. |
| Outcome: | The proposed method outperforms deep learning and deep learning methods with and without dialog context for ghosting. |
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing dialogue models struggle to interpret context accurately due to irrelevant or misclassified knowledge, limiting their effectiveness in real-world scenarios. |
| Approach: | They propose a framework that dynamically filters relevant commonsense knowledge and extracts personalized information to improve empathetic dialogue generation. |
| Outcome: | The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset. |
Grounding Conversations with Improvised Dialogues (2020.acl-main)
Copied to clipboard
| Challenge: | Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people. |
| Approach: | a new study uses a corpus of yes-and-turns to analyze improv dialogues . they find that dialogue is a collaborative process by which partners coordinate via turns or acts to jointly construct a common world state. |
| Outcome: | a new study fine-tunes chit-chat dialogue systems with their corpus to encourage more grounded, relevant conversation. |
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue (2024.acl-long)
Copied to clipboard
| 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. |
REAM♯: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation metrics for open-domain dialogue systems are limited by the diversity of possible outcomings. |
| Approach: | They propose a method to augment a reference set to improve reliability . they propose BLEU to measure similarity between a predicted response and a small set of references . |
| Outcome: | The proposed model improves the reliability of reference-based metrics with augmented reference sets. |
Neural Generation of Dialogue Response Timings (2020.acl-main)
Copied to clipboard
| Challenge: | Using neural models, the timings of spoken response offsets in human dialogue can vary based on contextual elements of the dialogue. |
| Approach: | They propose neural models that simulate the distributions of response offsets taking into account the response turn as well as the preceding turn. |
| Outcome: | The proposed models can generate distributions of response offsets based on the response turn and preceding turn based upon human listening tests and offline experiments. |
Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing studies show that large pre-trained language models can be adapted to task-oriented dialog systems. |
| Approach: | They propose to use contextual dynamic prompting to generate prompts in dialogs . they propose to distill useful prompting signals from dialog contexts based on contextual dynamic . |
| Outcome: | The proposed approach improves response generation by 3 points and 17 points when dialog states are incorporated. |
Think Beyond Words: Exploring Context-Relevant Visual Commonsense for Diverse Dialogue Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to generate intelligent open-domain dialogue agents only consider auxiliary commonsense stored in pure text, ignoring grounding information from the external visual world. |
| Approach: | They propose a VIsual Commonsense enhanced dialogue generaTOR that exploits auxiliary commonsense from images related to context to generate coherent and informative responses. |
| Outcome: | The proposed method outperforms the latest competitive methods in terms of coherence and diversity on two public datasets. |
Building a Database of Conversational Routines (2024.lrec-main)
Copied to clipboard
| Challenge: | The Routinicon is a constructicographic resource for the description of conversational routines in Russian language. |
| Approach: | They propose to use the Routinicon to collect and systematically describe conversational routines in Russian language. |
| Outcome: | The proposed resource is a natural extension of the Russian Constructicon and Pragmaticon projects. |
Dialogue-oriented Pre-training (2021.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models (PrLMs) have shown impressive improvements for various downstream tasks including various dialogue related ones. |
| Approach: | They propose to use pre-trained language models to simulate dialogue features on general plain text with common language model training objectives to improve performance. |
| Outcome: | The proposed method is fine-tuned on three public multi-turn dialogue datasets and achieves significant and consistent improvement over the plain PrLMs. |
CAMAL: A Novel Dataset for Multi-label Conversational Argument Move Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models that combine CNN and LSTM structures with speaker ID graphs improve the F1-score of our baseline models to detect speakers’ intents by a large margin. |
| Approach: | They propose a conversational multi-label corpus of teaching transcripts for Conversational Argument Move AnaLysis (CAMAL) the dataset includes 165 discussion transcripts facilitated by pre-service teachers and students . |
| Outcome: | The proposed model improves the F1-score of the baseline model to detect speakers’ intents by a large margin. |
A Theoretically Grounded Approach to Summarizing Conversation Dynamics for Forecasting the Derailment of Online Conversations (2026.acl-long)
Copied to clipboard
| Challenge: | Recent work on conversation derailment prediction relies on linguistic features rooted in linguistic and social theories. |
| Approach: | They propose a system that predicts from the start of a conversation whether it will derail into toxic exchanges. |
| Outcome: | The proposed system achieves 10% performance increase over baseline and 6.47% increase on benchmark dataset. |
Towards Faithful Dialogues via Focus Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge-grounded models rely on elaborate data engineering or increasing the model’s parameters ignoring to track the tokens that significantly influence losses, which is decisive for the optimization direction of the model in each iteration. |
| Approach: | They propose a novel learning approach that adjusts the contribution of each token to the optimization direction by directly scaling the corresponding objective loss. |
| Outcome: | The proposed approach achieves the new state-of-the-art results and generates more reliable responses while maintaining training stability. |
Using Lexical Alignment and Referring Ability to Address Data Sparsity in Situated Dialog Reference Resolution (D18-1)
Copied to clipboard
| Challenge: | Existing work on exophoric reference resolution for situated dialogs is limited to a literary model . et al., 2010) showed that it is possible to improve dialogic reference resolving by incrementally adapting word semantic model parameters to idiosyncratic language use by dyad partners. |
| Approach: | They propose to use a logistic regression model to adapt a model to idiosyncratic language . they first train a log regression model and then use it to learn the general referring ability of each word . |
| Outcome: | The proposed methods improve dialogic reference resolution without annotation of referring expressions even with little background data. |
Domain Adaptive Dialog Generation via Meta Learning (P19-1)
Copied to clipboard
| Challenge: | Existing methods to adapt dialog data to new domains with limited resources are expensive . a domain adaptive dialog system model learns from multiple rich-resource tasks and adapts to new tasks with minimal training samples. |
| Approach: | They propose a domain adaptive dialog generation method based on meta-learning . they train a dialog system model using multiple rich-resource single-domain dialog data . |
| Outcome: | The proposed method can learn a competitive dialog system on a new domain with minimal training examples. |
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing systems that negotiate with humans have broad applications in pedagogy and conversational AI. |
| Approach: | They propose to annotate persuasion strategies and perform correlation analysis to understand how dialogue behaviors are associated with the negotiation performance. |
| Outcome: | The proposed system improves negotiation performance for all strategies labeled as skewed . the proposed system is available on github.com/kushalchawla/ . |
Show or Tell? Modeling the evolution of request-making in Human-LLM conversations (2026.findings-eacl)
Copied to clipboard
| Challenge: | a new framework to describe request-making segments user input into request content, roles assigned, query-specific context, and task-independent expressions. |
| Approach: | They propose a framework to describe request-making that segments user input into request content, roles assigned, query-specific context, and the remaining task-independent expressions. |
| Outcome: | The proposed framework reveals fundamental and habitual user-LLM interaction patterns beyond individual task completion. |
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for knowledge selection focus on relevance between knowledge and dialogue context, ignoring personal preference for knowledge. |
| Approach: | They propose to introduce personal memory into knowledge selection in chatbots to address personalization issue by integrating personal memory and inverse mapping into a closed loop. |
| Outcome: | The proposed method outperforms existing methods significantly on automatic evaluation and human evaluation. |
Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing personality assessment datasets based on natural language do not consider interactivity. |
| Approach: | They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment. |
| Outcome: | The proposed dataset contains 1260 interaction rounds between humans and agents with different personalities. |
X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents (2025.acl-long)
Copied to clipboard
| Challenge: | Traditional Turing test limits each participant to one message at a time and requires constant human participation. |
| Approach: | They propose to enhance the original Turing test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
| Outcome: | The proposed test improves the original test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents (C18-1)
Copied to clipboard
| Challenge: | Existing methods for DA annotation are incompatible with each other and do not cover all aspects necessary for open-domain human-machine interaction. |
| Approach: | They propose to map publicly available corpora to a subset of the ISO standard and create a task-independent training corpus for DA classification. |
| Outcome: | The proposed method can train a domain-independent DA tagger on out-of-domain conversational data and achieve robustness across different DA categories. |
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent (2025.findings-acl)
Copied to clipboard
Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong
| Challenge: | Recent advances in Large Language Models (LLMs) have propelled the development of Conversational Recommendation Agents (CRAs). |
| Approach: | They propose a multi-turn preference optimization paradigm that leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turned dialogues. |
| Outcome: | The proposed paradigm eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements. |
An LLM Feature-based Framework for Dialogue Constructiveness Assessment (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on dialogue constructiveness assessment focus on analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness. |
| Approach: | They propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature- and neural approaches while mitigating their downsides. |
| Outcome: | The proposed framework outperforms standard feature-based models and neural models on three dialogue constructiveness datasets. |
Exophoric Pronoun Resolution in Dialogues with Topic Regularization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on pronoun coreference resolution focus on anaphora and cataphores . exophoric pronounos are common in daily communications, but can be disambiguated by general topics of the dialogue. |
| Approach: | They propose to leverage local context and global topics of dialogues to solve out-of-text PCR problem by adding topic regularization. |
| Outcome: | Extensive experiments show that topic regularization can be used to solve the out-of-text PCR problem. |
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing variational dialog models have pre-trained, restricting diversity of responses . a diffusion model increases complexity of prior distribution and its compatibility with PLMs . |
| Approach: | They propose a hierarchical conditional variational autoencoder with diffusion priors to address these challenges. |
| Outcome: | The proposed method generates more diverse responses without dialog pre-training. |
A recipe for annotating grounded clarifications (2021.naacl-main)
Copied to clipboard
| Challenge: | In order to interpret communicative intents of an utterance, it needs to be grounded in world modalities. |
| Approach: | They propose a recipe for obtaining grounding annotations for dialogue clarification mechanisms that make explicit the process of interpreting communicative intents of an utterance. |
| Outcome: | The proposed method is based on the definitions of perceptual and collaborative grounding and on the classification of clarification phenomena. |
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)
Copied to clipboard
Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao
| Challenge: | Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue. |
| Approach: | They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions. |
| Outcome: | The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses. |
CharacterCraft: Bridging the Literature-Reality Dialogue Gap for Practical Role-Playing Agents (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing dialogue datasets have a bias between query distributions and real-world user language usage. |
| Approach: | They propose a framework for Chinese role-playing and a robust evaluation method . they propose specialized Chinese dialogue extraction model and specialized memory retrieval module . |
| Outcome: | The proposed framework extracts character dialogue from novels and ensures high data quality. |
LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback (2024.naacl-long)
Copied to clipboard
| 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. |
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods of open-domain dialogue evaluation are labor-intensive and inefficient. |
| Approach: | They propose to use open-domain dialogues to evaluate different aspects of dialogues using holistic evaluation metrics. |
| Outcome: | The proposed metrics show strong correlations with human judgments. |
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to interpret task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. |
| Approach: | They propose a neuro-symbolic approach that performs explicit reasoning that justifies model decisions by reasoning chains. |
| Outcome: | The proposed approach achieves better results and introduces an interpretable decision process. |
Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation (2021.findings-acl)
Copied to clipboard
| Challenge: | Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. |
| Approach: | They propose methods for automatically creating adversarial negative training data . they use mask-and-fill and keyword-guided approaches to generate negative examples . |
| Outcome: | The proposed approaches outperform baseline models in providing informative negative examples for training dialogue systems. |
Time-Considerable Dialogue Models via Reranking by Time Dependency (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing dialogue models do not consider time information that humans are constantly aware of. |
| Approach: | They propose to categorize responses by their naturalness at different times and introduce a new metric to classify responses into categories. |
| Outcome: | The proposed model categorizes responses by their naturalness at different times and evaluates them subjectively. |
Multimodal Conversation Structure Understanding (2026.eacl-long)
Copied to clipboard
| Challenge: | a new set of tasks is being developed to parse the structure of conversation . female characters are 1.2 times more likely to be cast as an addressee or side-participant . |
| Approach: | They propose a set of tasks and release an annotated dataset for multimodal conversation structure understanding. |
| Outcome: | The proposed model outperforms the baseline model, but performance drops when character identities are anonymized. |
Probing Commonsense Explanation in Dialogue Response Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Currently, response generation (RG) models do not understand human communication intents. |
| Approach: | They propose to examine commonsense reasoning implicitly to determine whether RG models produce coherent responses in conversations. |
| Outcome: | The proposed probing settings show that RG models fail to capture the logical relations between commonsense explanations and responses and fine-tuning on in-domain data do not lead to understanding of CSR for RG. |
Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation (N19-1)
Copied to clipboard
| Challenge: | Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. |
| Approach: | They propose to rerank a sequence-to-sequence model to improve the informativeness, reasonableness, and grammatically of responses by using externally-trained classifiers targeting each of these factors. |
| Outcome: | The proposed model improves the informativeness, reasonableness, and grammatically of responses. |
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding (2023.findings-acl)
Copied to clipboard
Hao Fang, Anusha Balakrishnan, Harsh Jhamtani, John Bufe, Jean Crawford, Jayant Krishnamurthy, Adam Pauls, Jason Eisner, Jacob Andreas, Dan Klein
| Challenge: | In a task-oriented dialogue system, response generation is a conditional language model, but effective dialogue agents must balance fluent generation with stricter constraints. |
| Approach: | They propose a rule-based content selection model that transduces a dialogue agent’s actions and their results into context-free grammars representing the space of contextually acceptable responses. |
| Outcome: | The proposed architecture outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness. |
Beyond Goldfish Memory: Long-Term Open-Domain Conversation (2022.acl-long)
Copied to clipboard
| Challenge: | Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. |
| Approach: | They propose to use retrieval-augmented methods to summarize and recall past conversations to improve their models. |
| Outcome: | The proposed models outperform the current state-of-the-art models on human-human chat sessions in both automatic and human evaluations. |
Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System (2020.coling-main)
Copied to clipboard
| Challenge: | Conventional neural generative models generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. |
| Approach: | They propose a method that employs topical constraint and semantic constraint to generate relevant responses by regularizing the decoding objective function with semantic distance. |
| Outcome: | The proposed method generates more topic-relevant and content-rich responses than conventional models. |
EnDex: Evaluation of Dialogue Engagingness at Scale (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models that measure engagement use expensive human annotas and abstract definitions of the term. |
| Approach: | They propose a human-reaction based model to evaluate dialogue engagingness . they propose combining distant-supervision with a theoretical foundation for engagement . |
| Outcome: | The proposed model is trained on 80k Reddit-based engagement datasets . it uses distant-supervision from human-reaction feedback to evaluate dialogue engagementness . |
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)
Copied to clipboard
| Challenge: | Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice. |
| Approach: | They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework. |
| Outcome: | The proposed model can be applied to various downstream dialogue tasks. |
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)
Copied to clipboard
| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
| Approach: | They propose an iterative emotion interaction network which uses iterativly predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. |
| Outcome: | The proposed method retains state-of-the-art performance on two datasets and achieves high accuracy. |
Bazinga! A Dataset for Multi-Party Dialogues Structuring (2022.lrec-1)
Copied to clipboard
Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, Claude Barras
| Challenge: | a dataset of 16 TV and movie series is filled with challenging multi-party dialogues. |
| Approach: | They propose a dataset built around 16 TV and movie series with challenging multi-party dialogues. |
| Outcome: | The proposed dataset is a step towards better multi-party dialogue structuring and understanding. |
Persona-Consistent Dialogue Generation via Pseudo Preference Tuning (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for improving persona consistency in dialogues require external resources. |
| Approach: | They propose a method for enhancing persona consistency in dialogue response generation using direct preference optimization using persona data. |
| Outcome: | The proposed method produces more consistent and natural responses than previous methods. |
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding (N19-1)
Copied to clipboard
| Challenge: | Existing models that use contextual information of dialogues to improve spoken language understanding (SLU) select the wrong history when the histories are similar in content. |
| Approach: | They propose time-aware models that automatically learn the latent time-decay function of the history without a manual time- decay. |
| Outcome: | The proposed models achieve higher F1 scores than state-of-the-art models on a benchmark dataset . |
CTSM: Combining Trait and State Emotions for Empathetic Response Model (2024.lrec-main)
Copied to clipboard
| Challenge: | Empathetic response generation attempts to empower dialogue systems to perceive speakers’ emotions and generate empathetic responses accordingly. |
| Approach: | They propose to combine trait and state emotions for Empathetic Response Model to enable dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates more empathetic responses. |
Topic Spotting using Hierarchical Networks with Self Attention (N19-1)
Copied to clipboard
| Challenge: | Existing systems struggle to have consistent long term conversations with the users and fail to build rapport. |
| Approach: | They propose a hierarchical model with self attention for topic spotting . they compare it to previous proposed techniques for topic detection . |
| Outcome: | The proposed model outperforms existing models for topic spotting and deep models for text classification in an online setting. |
PRIDE: Predicting Relationships in Conversations (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extracting interpersonal relationships from dialogues are limited to end-to-end learning. |
| Approach: | They propose a neural multi-label classifier that infers relationships from dialogues by external knowledge about speaker features and conversation style. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on large-scale datasets with directed relationships of conversation participants. |
Partner Personas Generation for Dialogue Response Generation (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing frameworks that focus on self personas ignore the value of partner persona . experimental results show that our framework generates relevant, interesting, coherent and informative partner personages even compared to ground truth partner personagers. |
| Approach: | They propose a framework that leverages automatic partner personas generation to enhance dialogue response generation. |
| Outcome: | The proposed framework generates relevant, interesting, coherent and informative partner personas even compared to ground truth partner person . it surpasses baselines that condition on ground truth persona . |
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data (2023.emnlp-main)
Copied to clipboard
| Challenge: | Despite the promising potential of chat models, they are only accessible through restricted APIs, creating barriers for new research and progress in the field. |
| Approach: | They propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself. |
| Outcome: | The proposed pipeline generates a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself, simulating both user and AI responses. |
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts (2021.emnlp-main)
Copied to clipboard
| Challenge: | despite progress toward data-driven conversational agents, dialogue models still suffer from issues surrounding safety and offensive language. |
| Approach: | They analyze reddit threads and reddits to determine the stance of offensive dialogue models . they find 42% of human responses agree with toxic comments, compared to 13% with safe comments . |
| Outcome: | The proposed model produces 29% fewer offensive replies than the baseline model. |
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to rewrite context-dependent queries lack sufficient information for optimal retrieval performance. |
| Approach: | They propose to use large language models (LLMs) as query rewriters to generate informative queries through well-designed instructions. |
| Outcome: | The proposed approach improves performance on the QReCC dataset compared to human rewrites . |
MRF-Chat: Improving Dialogue with Markov Random Fields (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to deep learning for open-domain dialogue include training end-to-end models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts and persona of the agent and the user, among others. |
| Approach: | They propose a probabilistic approach using Markov Random Fields to augment existing deep-learning methods for improved next utterance prediction. |
| Outcome: | The proposed approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents. |
SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Simulated Multiple Reference Training (SMRT) improves non-task-oriented dialog models by reducing the need for related-domain dialog data. |
| Approach: | They apply Simulated Multiple Reference Training (SMRT) to chatbots to overcome sparse dialog data. |
| Outcome: | The proposed model outperforms pretraining on human evaluation quality and lexical diversity without requiring related-domain dialog data. |
Language Model Detoxification in Dialogue with Contextualized Stance Control (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on Language Model detoxification has focused on reducing the toxicity of the generation itself without consideration of the context. |
| Approach: | They propose a method to do context-dependent detoxification without taking into account the stance of the generated response. |
| Outcome: | The proposed method can learn the context-dependent stance control strategies while keeping a low self-toxicity of the underlying LM. |
Speculative Sampling in Variational Autoencoders for Dialogue Response Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have tried to improve variational models but they fail to learn proper mappings. |
| Approach: | They propose to use a variable-based sampling technique to find the most probable one from redundantly sampled latent variables to tie up the variable with a given response. |
| Outcome: | The proposed method is effective in response generation with massive dialogue data constructed from Twitter posts. |
SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation (2026.findings-acl)
Copied to clipboard
| 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. |
Unsupervised Learning of Hierarchical Conversation Structure (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Goal-oriented conversations often have sub-dialogue structure, but it can be domain-dependent . Increasingly, language understanding applications involve conversational speech and text . |
| Approach: | They propose an unsupervised approach to learning hierarchical conversation structure . they use turn and sub-dialogue segment labels to decode the structure based on dialogue acts and subtasks . |
| Outcome: | The proposed approach improves neural models for three conversation-level understanding tasks. |
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch (2024.acl-long)
Copied to clipboard
Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song
| Challenge: | Prior studies on stickers focused on sentiment analysis and recommendation systems, overlooking their vast potential in empathetic response generation. |
| Approach: | They propose a multimodal empathetic dialogue dataset, STICKERCONV, which simulates human behavior with stickers, and propose evaluative metrics based on LLM. |
| Outcome: | The proposed framework generates contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging e-dialog systems. |
A Taxonomy of Empathetic Response Intents in Human Social Conversations (2020.coling-main)
Copied to clipboard
| Challenge: | Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. |
| Approach: | They aim to combine dialogue act/intent modelling and neural response generation to produce a large-scale taxonomy for empathetic response intents. |
| Outcome: | The proposed method improves the response quality of chatbots and makes them more controllable and interpretable. |
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (D18-1)
Copied to clipboard
| Challenge: | Existing encoder-decoder models for open domain dialogue generate generic, uninformative, and non-coherent responses. |
| Approach: | They propose to introduce a measure of coherence as the GloVe embedding similarity between dialogue context and generated response to improve output diversity. |
| Outcome: | The proposed model improves on the OpenSubtitles corpus in terms of BLEU score and diversity metrics. |
Contextual Knowledge Learning for Dialogue Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. |
| Approach: | They propose a method to incorporate conversational context and knowledge into dialogue generation models . they use Latent Vectors to capture the relationship between context and knowing . |
| Outcome: | The proposed approach improves performance with two standard datasets and human evaluations. |
One Battle After Another: Probing LLMs’ Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for instruction-following in multi-topic dialogues are limited to a fixed number of turns, susceptible to saturation and failing to account for users’ interactive experience. |
| Approach: | They propose a framework featuring a three-layer tracking mechanism and a query synthesis agent to mimic sequential user behaviors. |
| Outcome: | The proposed framework outperforms existing benchmarks in the evaluation of instruction following in multi-topic dialogues and demonstrates deficiencies in failure recovery and fine-grained instruction following. |
Adversarial Learning on the Latent Space for Diverse Dialog Generation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for dialog generation generate generic utterances, e.g., always generating "I don't know" |
| Approach: | They propose a framework that uses generative adversarial nets to generate conditioned responses in dialogs. |
| Outcome: | The proposed model generates more fluent, relevant, and diverse responses than state-of-the-art methods. |
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)
Copied to clipboard
| Challenge: | Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task. |
| Approach: | They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs. |
| Outcome: | The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs. |
Controllable Abstractive Dialogue Summarization with Sketch Supervision (2021.findings-acl)
Copied to clipboard
| Challenge: | Using a model to generate summary sketches, we improve abstractive dialogue summarization quality and enable granularity control. |
| Approach: | They propose a model that generates a preliminary summary sketch and a strategy to control granularity. |
| Outcome: | The proposed model achieves state-of-the-art on the largest dialogue summarization corpus with as high as 50.79 in ROUGE-L score. |
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)
Copied to clipboard
| Challenge: | Existing memory networks do not perform well when leveraging heterogeneous information from different sources. |
| Approach: | They propose to use user utterances, dialogue history and background knowledge tuples to integrate external knowledge into a neural dialogue model. |
| Outcome: | The proposed model outperforms the state-of-the-art data-driven task-oriented dialogue models on real-world datasets. |
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation (D18-1)
Copied to clipboard
| Challenge: | Sequence-to-Sequence models favor short generic responses . however, the model is not suitable for modeling dialogues . |
| Approach: | They propose a model that connects preceding and following conversations to a prior distribution to avoid non-differentiability of discrete natural language tokens. |
| Outcome: | The proposed model is highly efficient in learning the backbone of human-computer communications, but favors short generic responses. |
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences. |
| Approach: | They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. |
| Outcome: | The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations. |
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change. |
| Approach: | They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes . |
| Outcome: | The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation. |
Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop (D19-1)
Copied to clipboard
| Challenge: | Recent efforts focused on detecting antisocial behavior after the fact . a forecasting model needs to capture the flow of the conversation, not individual comments . real conversations have an unknown horizon; therefore a practical forecasting system needs to assess the risk . |
| Approach: | They propose a conversational forecasting model that learns conversational dynamics and exploits it to predict derailment as the conversation develops. |
| Outcome: | The proposed model outperforms state-of-the-art models at forecasting derailment . it learns an unsupervised representation of conversational dynamics and exploits it to predict future derailments . |
A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems (2021.emnlp-main)
Copied to clipboard
Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya
| Challenge: | Existing methods to improve NLU are laborintensive and expensive. |
| Approach: | They propose a scalable and automatic approach to improving NLU in a large-scale conversational AI system by leveraging implicit user feedback. |
| Outcome: | The proposed framework improves NLU in a large-scale conversational AI system across 10 domains. |
Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluations of dialogue quality rely on human judgments, which are time-consuming, labor-intensive, prone to biases, and lacking objectivity. |
| Approach: | They propose a method that utilizes the underlying patterns of dialogue act transitions to evaluate the appropriateness of chatbot responses. |
| Outcome: | The proposed method proves that human judgments are time-consuming, labor-intensive, and lacking objectivity. |
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful. |
| Approach: | They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation. |
| Outcome: | The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. |
MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing evaluation metrics for memory-augmented dialogue systems lack practical value . current evaluation methods only consider passive memory retrieval while ignoring diverse memory recall with rich triggering factors. |
| Approach: | They propose to use long-term memory to create human-like dialogues using chatbots. |
| Outcome: | The proposed benchmark covers memory retrieval and memory recognition tasks with both passive and proactive memory recall data. |
Online Conversation Disentanglement with Pointer Networks (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for disentangling textual conversations rely on dataset specific features that hinder generalization and adaptability. |
| Approach: | They propose an end-to-end online framework for conversation disentanglement that embeds the whole utterance that comprises timestamp, speaker, and message text. |
| Outcome: | The proposed method performs state-of-the-art on the Ubuntu IRC dataset and on other social and organizational platforms. |
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state- of-the art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial. |
| Approach: | They propose to integrate topical information into knowledge-grounded task-oriented dialogue systems by using multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources. |
| Outcome: | The proposed model outperforms existing models in knowledge selection and response generation. |
Training Neural Response Selection for Task-Oriented Dialogue Systems (P19-1)
Copied to clipboard
Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | Despite their popularity, retrieval-based models have had modest impact on task-oriented dialogue systems . main obstacle to their application is the low-data regime of most task-orientated dialogue tasks . e-commerce, banking, and other domains are applications of retrieval models . |
| Approach: | They propose a method which pretrains a retrieval-based model on large general-domain conversational corpora and fine-tunes it for the target dialogue domain. |
| Outcome: | The proposed method is evaluated on five diverse domains, ranging from e-commerce to banking. |
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (P19-1)
Copied to clipboard
| Challenge: | Existing models for SLU use explicit memory representations, but the context memory is under-exploited. |
| Approach: | They propose a dialogue logistic inference task to consolidate the context memory with SLU in a multi-task framework. |
| Outcome: | The proposed model improves slot filling and domain classification performance in a multi-task framework. |
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy (2025.naacl-long)
Copied to clipboard
| Challenge: | Motivational Interviewing (MI) is gaining attention as a theoretical basis for mental health chatbots. |
| Approach: | They propose a framework that simulates MI sessions enriched with the expertise of professional therapists by using large language models to generate utterances through prompt engineering. |
| Outcome: | The proposed framework simulates MI sessions enriched with the expertise of professional therapists and employs large language models to generate utterances through prompt engineering. |
Personalizing Dialogue Agents via Meta-Learning (P19-1)
Copied to clipboard
| Challenge: | Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. |
| Approach: | They propose to extend Model-Agnostic Meta-Learning (MAML) to personalized dialogue learning without using persona descriptions. |
| Outcome: | The proposed model outperforms baseline models in terms of human-evaluated fluency and consistency on a persona-chat dataset. |
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies on ERC lack the ability to extract and integrate emotional clues from the conversational context. |
| Approach: | They propose a new model that uses multi-turn reasoning modules to extract and integrate emotional clues from conversational context. |
| Outcome: | The proposed model outperforms existing models on three public benchmark datasets and is highly effective and superior to existing models. |
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020.acl-main)
Copied to clipboard
| 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. |
Dialog-Post: Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training (2023.acl-long)
Copied to clipboard
| Challenge: | a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks . |
| Approach: | They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively . |
| Outcome: | The proposed method outperforms existing models and achieves a 3.3% improvement on average. |
Improving Neural Conversational Models with Entropy-Based Data Filtering (P19-1)
Copied to clipboard
| Challenge: | Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. |
| Approach: | They propose an unsupervised method of filtering dialog datasets by removing generic utterances from training data using an entropy-based approach that does not require human supervision. |
| Outcome: | The proposed method improves dialog quality as chatbots learn to output more diverse responses to open-ended utterances. |
Book2Dial: Generating Teacher Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbots (2024.findings-acl)
Copied to clipboard
| Challenge: | Educational chatbots are a promising tool for assisting student learning, but high-quality data is difficult to obtain due to privacy concerns. |
| Approach: | They propose a framework for generating synthetic teacher-student interactions grounded in a set of textbooks and propose to open-source their results. |
| Outcome: | The proposed framework captures a key aspect of learning interactions where curious students with partial knowledge ask teachers questions about the material in the textbook. |
“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
| Approach: | They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles. |
| Outcome: | The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method. |
NatCS: Eliciting Natural Customer Support Dialogues (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing task-oriented dialogue datasets do not reflect the expected characteristics of real customer support conversations. |
| Approach: | They propose to collect real customer service conversations from real conversations . they show that dialogue act annotations provide more effective training data . |
| Outcome: | The proposed approach is more representative of real human-to-human conversations compared to existing dialogue datasets . the proposed approach can be used to facilitate open research in natural dialog systems . |
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup (2023.emnlp-main)
Copied to clipboard
| Challenge: | Disfluency detection models focus on individual utterances, but discontinuities in spoken transcripts occur across multiple turns. |
| Approach: | They propose a multi-turn "cleanup task" to detect discontinuities in spoken conversations . they leverage two modeling approaches for experimental evaluation as benchmarks . |
| Outcome: | The proposed task detects "discontinuities" in spoken conversations that can be removed . the results are compared with existing methods and are expected to be validated in the future . |
Emotion–Cause Pair Extraction in Conversations via Semantic Decoupling and Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue. |
| Approach: | They propose a framework for Emotion-Cause Pair Extraction in Conversations that decouples emotion-oriented semantics from cause-oriented ones and employs optimal transport to enable many-to-many and globally consistent emotion-cause matching. |
| Outcome: | The proposed framework achieves state-of-the-art on several benchmark datasets. |
ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)
Copied to clipboard
Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang
| Challenge: | Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers. |
| Approach: | They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach. |
| Outcome: | The proposed method outperforms manual methods and outperfies baselines on Taobao in China. |
Proxy Indicators for the Quality of Open-domain Dialogues (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for evaluation of open-domain dialogues are expensive and require human annotators to evaluate their quality. |
| Approach: | They propose to use a deep-learning model trained on the general language understanding evaluation benchmark to serve as a quality indication of open-domain dialogues. |
| Outcome: | The proposed model can infer various quality metrics and derive a component-based overall score. |
Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation (2025.coling-main)
Copied to clipboard
| Challenge: | KEEM is a dynamically generated dataset designed to enhance memory updates in long-term conversational systems. |
| Approach: | They propose a dataset that keeps emotional and essential memories and generates integrative memories that incorporate emotional context and causal relationships. |
| Outcome: | The Keep Emotional and Essential Memory (KEEM) dataset enhances memory updates in long-term conversational systems. |
Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversations (2024.emnlp-main)
Copied to clipboard
| Challenge: | Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs. |
| Approach: | They propose a methodological pipeline to investigate model performance across structural attributes of conversations. |
| Outcome: | The proposed method analyzes the performance of an LLM to classify multi-party conversations . it shows that response selection relies more on the textual content of conversations compared to addressee recognition . |
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing metrics for faithfulness of response are not aligned with human judgments. |
| Approach: | They propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue. |
| Outcome: | The proposed metric improves on BEGIN benchmarks and shows that it generates more faithful responses than standard decoding techniques. |
Red Teaming Language Models for Processing Contradictory Dialogues (2024.emnlp-main)
Copied to clipboard
| Challenge: | a recent study shows that language models are prone to self-contradiction during dialogues. |
| Approach: | They propose a red teaming framework that detects and attempts to explain dialogues, then modifies existing contradictory content using the explanation. |
| Outcome: | The proposed task improves the ability to detect contradictory dialogues and provides valid explanations. |
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset (2020.emnlp-main)
Copied to clipboard
| Challenge: | doc2dial dataset is a goal-oriented document-grounded dialogue model . it is based on how the authors compose documents for guiding end users . |
| Approach: | They propose a dataset of goal-oriented dialogues grounded in documents . they use annotated conversations with an average of 14 turns to generate conversational utterances . |
| Outcome: | The proposed dataset includes over 4500 annotated conversations with an average of 14 turns grounded in over 450 documents from four domains. |
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities. |
| Approach: | They propose a framework that integrates emotion correlation learning, utilization, and supervising. |
| Outcome: | The proposed framework improves empathetic perception and expression on a humanized dialogue dataset. |
INSPIRED: Toward Sociable Recommendation Dialog Systems (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on recommendation dialog systems lack a study on communication strategies used by human speakers for making successful and persuasive recommendations. |
| Approach: | They propose to annotate a dataset of human-human movie recommendation dialogs with sociable recommendation strategies. |
| Outcome: | The proposed model outperforms the baseline model in automatic and human evaluation. |
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Social biases present in data are often directly reflected in the predictions of models trained on that data. |
| Approach: | They analyze gender bias in dialogue data and propose techniques to mitigate it . they use counterfactual data augmentation, targeted data collection, and bias controlled training . |
| Outcome: | The proposed techniques mitigate gender bias by balancing genderedness of generated dialogue utterances. |
Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language (2023.acl-long)
Copied to clipboard
| Challenge: | Domain adaptation (DA) techniques have been used to improve performance of NLP systems for healthcare tasks due to numerous complexities of data. |
| Approach: | They propose to use domain adaptation techniques to improve generalizability across diverse datasets for dementia detection. |
| Outcome: | The proposed model achieves a 22% increase in accuracy adapting from a conversational to task-oriented dataset compared to a jointly trained baseline. |
Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current efforts to bridge the two modes of interaction are reactive, focusing on responding to user inputs rather than coordinating dialogue flows. |
| Approach: | They propose a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows. |
| Outcome: | The proposed dataset outperforms baseline models in intent detection and mode transition handling. |
Exploring In-Context Learning for Knowledge Grounded Dialog Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge grounded dialog generation models are prone to hallucination and produce factually inaccurate outputs. |
| Approach: | They propose a retrieval-based framework which leverages in-context learning and retrieval techniques to enhance LLMs on knowledge grounded dialog generation. |
| Outcome: | The proposed framework outperforms existing training-based models on a large-scale knowledge graph with 1M+ facts and is expected to perform knowledge-intensive tasks. |
YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing conversational agents (CAs) are designed to satisfy user needs through user-driven interactions. however, many real-world settings, such as academic interviewing, require agents that can elicit information from users. |
| Approach: | They propose to support Information Elicitation Agents (IEAs) in which the agent’s goal is to elicit information from users to support the agent's institutional or task-oriented objectives. |
| Outcome: | The proposed agent-based model improves the performance of a 26M-token dataset of 2,281 human-to-human dialogues on multiple foundation LLMs and human evaluation confirms the results. |
An “Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives” (2023.emnlp-main)
Copied to clipboard
| Challenge: | Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. |
| Approach: | They review 534 papers on building mental health-related conversational agents . they recommend a few recommendations to bridge the disciplinary divide . |
| Outcome: | The systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. |
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking (2024.lrec-main)
Copied to clipboard
| Challenge: | Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations: i) erroneously presume a uniform distribution of slots throughout the dialog; ii) neglect to assign partial scores for individual turns; c) frequently overestimate or underestimate performance by repeatedly counting the models’ successful or failed predictions. |
| Approach: | They propose a new metric: Granular Change Accuracy (GCA) which evaluates the predicted changes in dialogue state over the entire dialogue history. |
| Outcome: | The proposed metric reduces biases arising from distribution uniformity and the positioning of errors across turns, resulting in a more precise evaluation. |
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System (2023.findings-acl)
Copied to clipboard
| Challenge: | End-to-end task-oriented dialogue systems are expensive to annotate and lack data in real scenarios. |
| Approach: | They propose to implement dual learning in task-oriented dialogues to exploit the correlation of heterogeneous data. |
| Outcome: | The proposed method improves the effectiveness of end-to-end task-oriented dialogue systems under multiple benchmarks and obtains state-of-the-art results in low-resource scenarios. |
LlamaPIE: Proactive In-Ear Conversation Assistants (2025.findings-acl)
Copied to clipboard
| Challenge: | LlamaPIE is the first real-time proactive assistant designed to enhance human conversations . it provides discreet, concise guidance delivered via hearable devices . traditional language models require explicit user invocation, but the assistant operates in the background . |
| Approach: | They propose a two-model pipeline that decides when to respond and a larger model that generates the response. |
| Outcome: | The proposed approach is effective in providing helpful, unobtrusive assistance on real-world datasets. |
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)
Copied to clipboard
Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots. |
| Approach: | They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios. |
| Outcome: | The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning . |
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality (2022.emnlp-main)
Copied to clipboard
| Challenge: | Currently, human communication models fail to explicitly model common ground (CG) . less than half of the responses in current data is rated as high quality . |
| Approach: | They propose a dataset that annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |
| Outcome: | The proposed dataset annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |
Disfluency Generation for More Robust Dialogue Systems (2023.findings-acl)
Copied to clipboard
| Challenge: | Disfluencies in user utterances can trigger a chain of errors impacting all the modules of a dialogue system. |
| Approach: | They propose to augment existing dialogue datasets with disfluent utterances by paraphrasing them into disfluente ones. |
| Outcome: | The proposed method improves dialogue state tracking and response generation by combining disfluent utterances with disfluency utteraces. |
Dicta-Sign-LSF-v2: Remake of a Continuous French Sign Language Dialogue Corpus and a First Baseline for Automatic Sign Language Processing (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing research on automatic Sign Language Processing (SLP) has focused on recognizing lexical signs, but other gestural units like iconic structures need to be recognized. |
| Approach: | They propose a public remake of the French Sign Language part of the Dicta-Sign corpus with clean annotations and a Convolutional-Recurrent Neural Network to train and test it. |
| Outcome: | The proposed version of the publicly available SL corpus Dicta-Sign is limited to its French Sign Language part and includes lexical and non-lexical annotations over 11 hours of video recording with 35000 manual units. |
FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval (2026.findings-acl)
Copied to clipboard
Caishuang Huang, Yang Qiao, Rongyu Zhang, Junjie Ye, Pu Lu, null Wuwenxi, Meng Zhou, Xiku Du, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing data synthesis methods rely on static tools to generate queries . this approach fails to capture the implicit, event-driven nature of real-world needs . |
| Approach: | They propose a forward synthesis framework to generate high-quality financial dialogues . they construct a repository of 43,066 tools and synthesize over 148k dialogue instances . |
| Outcome: | Experiments show that models trained on FinToolSyn achieve a 21.06% improvement . the framework is designed to generate high-quality financial dialogues . |
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue (2022.emnlp-main)
Copied to clipboard
Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang
| Challenge: | Prior studies of task transfer in dialogue consider only 2-4 tasks, focus on multitasks. |
| Approach: | They propose a benchmark for FEw-sample TAsk transfer in open-domain dialogue. |
| Outcome: | The proposed benchmark analyzes the transferability between 132 source-target task pairs and provides a baseline for future work. |
Fora: A corpus and framework for the study of facilitated dialogue (2024.acl-long)
Copied to clipboard
| Challenge: | a new study of facilitated dialogues focuses on the sharing of personal experience . social media is a popular method of civic engagement but lacks the tools to analyze it . |
| Approach: | They compile 262 facilitated conversations hosted with partner organizations . they taxonomize personal sharing behaviors and facilitation strategies in the corpus . |
| Outcome: | The proposed framework can be used to analyze facilitated dialogues and parse spoken conversations . the data can be applied to other fields, including civic use in governance and social science . |
IM^2: an Interpretable and Multi-category Integrated Metric Framework for Automatic Dialogue Evaluation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Evaluation metrics for dialogue systems are expensive and time-consuming . current evaluation metrics focus on a single quality or several qualities . |
| Approach: | They propose an interpretable, multi-faceted, and controllable framework to combine dialogue metrics which are good at measuring different qualities. |
| Outcome: | The proposed framework integrates a large number of evaluation metrics to improve the performance of the model. |
Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models focus on identifying specific types of dialogue knowledge and utilizing corresponding datasets for training, but lack generalization capabilities and computational resources. |
| Approach: | They propose a framework that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and exploits it for response generation. |
| Outcome: | The proposed framework exploits multi-source multi-type knowledge from LLMs to generate coherent, informative, and fluent responses. |
Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework (2023.acl-long)
Copied to clipboard
| Challenge: | Current evaluations of FEC models that depend on factuality metrics are not reliable and detailed enough. |
| Approach: | They propose a fine-grained evaluation framework that automatically evaluates FEC models on different error categories. |
| Outcome: | The proposed evaluation framework compares models on different error categories and finds the best training modes and significant differences in the performance of existing models. |
Can LLMs Understand the Implication of Emphasized Sentences in Dialogue? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Emphasis is a crucial component in human communication, which indicates speaker’s intention and implication beyond pure text in dialogue. |
| Approach: | They propose a benchmark dataset with annotated dialogue samples capturing the implications of emphasis. |
| Outcome: | The proposed evaluation pipeline achieves high correlation with human scoring and commercial LLMs perform better than open-source LLM. |
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)
Copied to clipboard
| 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. |
MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization (2025.findings-emnlp)
Copied to clipboard
Yinhong Liu, Jianfeng He, Hang Su, Ruixue Lian, Yi Nian, Jake W. Vincent, Srikanth Vishnubhotla, Robinson Piramuthu, Saab Mansour
| Challenge: | Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications. |
| Approach: | They propose a meta-evaluation benchmark for multimodal dialogue summarization based on image-sharing dialogues, corresponding summaries and human judgments . |
| Outcome: | The proposed framework is the first to identify and formalize key evaluation dimensions specific to MDS. |
A Fully Automated Pipeline for Conversational Discourse Annotation: Tree Scheme Generation and Labeling with Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have shown promise in automating discourse annotation for conversations. |
| Approach: | They propose a pipeline that uses large language models to construct and perform annotations using speech functions and the Switchboard-DAMSL taxonomies. |
| Outcome: | The proposed pipeline outperforms existing tree annotation schemes and can match or surpass human annotations while significantly reducing time required for annotation. |
It’s Not under the Lamppost: Expanding the Reach of Conversational AI (2024.lrec-main)
Copied to clipboard
| Challenge: | Focused probes into the capabilities of language-based assistants easily reveal significant areas of brittleness that demonstrate large gaps in their coverage. |
| Approach: | They propose a process for collecting specific kinds of data to uncover these gaps and an annotation scheme for system responses. |
| Outcome: | The proposed system includes both Conventional and GenAI systems, including ChatGPT and Bard/Gemini. |
Persona-Grounded Safety Evaluation of AI Companions in Multi-Turn Conversations (2026.acl-long)
Copied to clipboard
| Challenge: | Existing safety evaluations rely on self-reported user data or interviews . a recent study evaluated how Replika responds to high-risk user groups . |
| Approach: | They propose a framework for controlled simulation and safety evaluation of multi-turn interactions with AI companion applications. |
| Outcome: | The proposed framework evaluates how Replika responds to high-risk user groups . it incorporates emotion modeling and LLM-assisted utterance-and harm-level classification . |
Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations (2023.emnlp-main)
Copied to clipboard
| Challenge: | open-domain chatbots focus on short single-session dialogue, neglecting the potential need for understanding contextual information in multiple consecutive sessions. |
| Approach: | They propose a 1M multi-session dialogue dataset for integrating time intervals and speaker relationships into a long-term conversation setup. |
| Outcome: | The proposed model can generate coherent responses according to time intervals and speaker relationships with high user engagement without contradiction in a long-term conversation setup. |
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions (2024.findings-emnlp)
Copied to clipboard
Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, Elizabeth Daly, David Piorkowski, John Richards
| Challenge: | Modern language models exhibit some inherent shortcomings, particularly in conversational settings. |
| Approach: | They propose a set of maxims for describing effective human-AI conversation that include quantity, quality, relevance, manner, benevolence, and transparency. |
| Outcome: | The proposed maxims are applied to human-AI interactions and are based on extensive research from the social science and AI communities. |
Chameleon LLMs: User Personas Influence Chatbot Personality Shifts (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have examined whether large language models adapt their perceived personalities in response to user interactions. |
| Approach: | They propose to use a controlled simulation to measure chatbot personality shifts before and after the interaction to determine whether LLMs exhibit conversational adaptations. |
| Outcome: | The proposed model exhibits personality adaptations over prolonged interactions, while Emotional Stability and Intellect remain relatively stable. |
Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to Emotional Support Conversation (ESC) are mechanistically opaque and lacks a causal mechanism between dialogue features and effective empathic strategies. |
| Approach: | They propose a framework that uses Doubly Robust learning to model causal effects of utterance features on strategy selection. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in empathy and helpfulness and provides a theoretically grounded, interpretable solution to the mechanistic interpretability dilemma in affective computing. |
Dual Process Masking for Dialogue Act Recognition (2024.findings-emnlp)
Copied to clipboard
Yeo Jin Kim, Halim Acosta, Wookhee Min, Jonathan Rowe, Bradford Mott, Snigdha Chaturvedi, James Lester
| Challenge: | Dialogue act recognition is the task of classifying conversational utterances based on their communicative intent or function. |
| Approach: | They propose a dual-processing approach that masks less important tokens in the input and enhances interpretability by using the masks applied during classification learning. |
| Outcome: | The proposed approach significantly improves performance over strong baselines for dialogue act recognition on a collaborative problem-solving dataset and three public dialogue benchmarks. |
Improving Dialogue Discourse Parsing through Discourse-aware Utterance Clarification (2025.acl-long)
Copied to clipboard
| Challenge: | Extensive experiments on the STAC and Molweni datasets demonstrate that our approach effectively resolves ambiguities and significantly outperforms the state-of-the-art (SOTA) baselines. |
| Approach: | They propose a Discourse-aware Clarification Module (DCM) that generates clarifications for the parser through systematic clarification type reasoning and discourse goal reasoning. |
| Outcome: | Extensive experiments on the STAC and Molweni datasets demonstrate that the proposed module significantly outperforms the state-of-the-art (SOTA) framework. |
PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture complexities of personalized task-oriented assistance. |
| Approach: | They propose a benchmark to evaluate personalization in task-oriented AI assistants . the benchmark features user profiles equipped with rich preferences and interaction histories . |
| Outcome: | The proposed benchmark features user profiles equipped with rich preferences and interaction histories . it also features a judge agent and user agent that employs the LLM-as-a-Judge paradigm . |
Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have attracted a lot of attention in the legal domain due to their ability to tackle a variety of legal tasks. |
| Approach: | They constructed a corpus consisting of two legal scenarios using the IRAC method and used it to perform analysis on the corpus. |
| Outcome: | The proposed model can analyze a contract act in Malaysia and the Australian Social Act for Dependent Child using the IRAC method. |
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2023.findings-emnlp)
Copied to clipboard
Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur
| Challenge: | Dialogue models are able to generate fluent and interesting responses, but they can be difficult to control and may produce non-engaging, unsafe results. |
| Approach: | They propose a framework for controlling dialogue model behavior using natural language rules, or guidelines, which provide information about the context they are applicable to and what should be included in the response. |
| Outcome: | The proposed framework is effective in three open-domain dialogue response generation tasks and is consistent with the developer's expectations and intent. |
Semantic-Aware Action Space Compression via LLM-DRL Synergy for Efficient Task-oriented Dialogue Policy Exploration (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained large language models (LLMs) with world knowledge and semantic understanding are promising for task-oriented dialogue systems. |
| Approach: | a framework that synergizes pre-trained large language models with DRL is proposed . a lightweight action pruning mechanism is employed to eliminate implausible actions . |
| Outcome: | a new framework synergizes pre-trained large language models with DRL to guide decision-making . the proposed framework eliminates semantically implausible or low-potential actions from multi-turn dialogue context . |
Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles (2025.acl-long)
Copied to clipboard
| Challenge: | Existing user simulators lack authenticity and user-level diversity in interactions with large language models. |
| Approach: | They propose a user simulator with implicit user profiles that infers user profiles from human-machine interactions to simulate personalized and realistic dialogues. |
| Outcome: | The proposed framework outperforms baselines in authenticity and diversity while maintaining comparable consistency. |
In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues (2025.findings-acl)
Copied to clipboard
Jon Cai, Brendan King, Peyton Cameron, Susan Windisch Brown, Miriam Eckert, Dananjay Srinivas, George Arthur Baker, V Kate Everson, Martha Palmer, James Martin, Jeffrey Flanigan
| Challenge: | Understanding speaker intentions remains a challenge in NLP . a number of corpora annotated using theoretical frameworks of dialogue focus on utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue. |
| Approach: | They propose to annotate a corpus of 33 dialogues and over 9,000 utterance units using the Dependency Dialogue Acts framework. |
| Outcome: | The proposed corpus spans four genres of multi-party conversations from different modalities. |
Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction (2025.acl-long)
Copied to clipboard
| Challenge: | Unlike traditional dialogue systems, goal-oriented proactive dialogue systems focus on achieving specific objectives by actively guiding and anticipating user needs. |
| Approach: | They propose a model-agnostic two-stage Consistency Reflection and Correction framework that allows the model to reflect on discrepancies between generated responses and dialogue contexts and suggest possible corrections. |
| Outcome: | The proposed framework significantly improves the consistency between generated responses and dialogue contexts on three datasets. |
From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset (2026.acl-long)
Copied to clipboard
| Challenge: | Applied Behavior Analysis (ABA) is the gold standard for clinical intervention, but large language models struggle to adhere to its standardized procedures. |
| Approach: | They propose a strategy-aware framework to unify high-fidelity intervention dialogue synthesis and clinical decision support. |
| Outcome: | Experiments show that ASDAgent achieves nearly 80% strategic consistency with human experts. |
Exploring the Role of Mental Health Conversational Agents in Training Medical Students and Professionals: A Systematic Literature Review (2025.findings-acl)
Copied to clipboard
| Challenge: | This systematic review analyses 38 studies on AI-powered conversational agents in mental health education and training . traditional training methods provide valuable but expensive and inherently limited learning opportunities . early pioneers like Woebot and Wysa demonstrated a groundbreaking insight: machines could engage in meaningful therapeutic interactions. |
| Approach: | They analyse 38 studies on AI-powered conversational agents in mental health education and training . findings reveal that AI-based approaches dominate the field, with training as the application area being the most prevalent . |
| Outcome: | The systematic review of 38 studies on AI-powered conversational agents in mental health education and training (MHET) reveals that AI-based approaches dominate the field, with training as the application area being the most prevalent. |
Are they lovers or friends? Evaluating LLMs’ Social Reasoning in English and Korean Dialogues (2026.acl-long)
Copied to clipboard
Eunsu Kim, Junyeong Park, Juhyun Oh, Kiwoong Park, Seyoung Song, A. Seza Doğruöz, Alice Oh, Najoung Kim
| Challenge: | Existing studies on LLMs' ability to infer social relationships have limited results for Korean and English. |
| Approach: | They propose a social reasoning task based on a 1.1k-dialogue dataset in English and Korean sourced from movie scripts to evaluate LLMs' ability to infer the social relationships between speakers. |
| Outcome: | The proposed task evaluates the ability of LLMs to infer the social relationships between speakers in 1.1k-dialogue datasets in English and Korean. |
Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing data selection methods struggle to distinguish learnable samples under contextual shifts. |
| Approach: | They propose a framework agnostic to underlying large language model-based conversational recommender systems (CRSs) that captures user preferences through free-form conversations and generates contextually relevant recommendations. |
| Outcome: | The proposed framework outperforms baselines on three CRS benchmarks with real-world temporal splits. |
Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current research treats personality recognition as a classification task, failing to reveal the supporting evidence for the recognized personality. |
| Approach: | They propose a task that aims to reveal the reasoning process as supporting evidence of the personality trait. |
| Outcome: | The proposed task reveals the reasoning process as supporting evidence of the personality trait. |
PSYDIAL: Personality-based Synthetic Dialogue Generation Using Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | a new pipeline for personality-based synthetic dialogues is being developed in Korea . a dataset curated by large language models is needed to generate human-like dialogues . |
| Approach: | They propose a personality-based synthetic dialogue data pipeline to elicit responses from large language models via prompting. |
| Outcome: | The proposed pipeline generates human-like dialogues considering real-world scenarios when users engage with chatbots. |
Thoughts to Target: Enhance Planning for Target-driven Conversation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results demonstrate that our method significantly improves the planning ability of LLMs, especially in target-driven conversations. |
| Approach: | They propose a two-stage framework to improve the LLMs’ capability in planning conversations towards designated targets by distilling natural language plans from a target-driven conversation corpus and generating new plans with demonstration-guided in-context learning. |
| Outcome: | The proposed framework improves the ability of conversational models to plan towards designated targets and can be used to build extensive conversational AI. |
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses. |
| Approach: | They propose a new method that enables LLMs to self-rank their responses without additional resources. |
| Outcome: | The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods. |
Stereotype or Personalization? User Identity Biases Chatbot Recommendations (2025.findings-acl)
Copied to clipboard
| Challenge: | We show that when people use large language models to generate recommendations, the LLMs produce responses that reflect both what the user wants and who the user is. |
| Approach: | They propose that chatbots should transparently indicate when user’s revealed identity influences model recommendations but fail to do so . |
| Outcome: | The proposed model generates racially stereotypical recommendations regardless of whether the user revealed their identity intentionally or unintentionally through implicit cues. |
DiaLLMs: EHR-Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing medical LLMs focus primarily on diagnosis recommendation, limiting their clinical applicability. |
| Approach: | They propose a medical LLM that integrates heterogeneous EHR data into clinically grounded dialogues. |
| Outcome: | The proposed model outperforms baselines in clinical test recommendation and diagnosis prediction. |
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment (2025.findings-acl)
Copied to clipboard
| Challenge: | Medical dialogue systems (MDS) struggle to identify relevant medical knowledge and generate accurate responses. |
| Approach: | They propose a medical dialogue system that integrates knowledge refining and dynamic prompt adjustment to improve medical knowledge and accuracy. |
| Outcome: | The proposed system outperforms state-of-the-art systems in both generation quality and medical entity accuracy. |
Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning. |
| Approach: | They propose to detect WMN indicators in conversations where a speaker signals the need to clarify or challenge word meaning. |
| Outcome: | The proposed models have better precision than previous regular expression based approaches and show some generalization abilities, but have moderate recall. |
TIGER: A Unified Generative Model Framework for Multimodal Dialogue Response Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing research on multimodal dialogues focuses on textual response generation and visual response selection based on the dialogue context. |
| Approach: | They propose a generative model framework for multimodal dialogue response generation that ground the conversation on an image. |
| Outcome: | The proposed system provides users with an enhanced conversational experience. |
Fairness Evaluation and Inference Level Mitigation in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, and the propagation of unwanted patterns during extended dialogues. |
| Approach: | They propose a pruning-based framework that detects context-aware neuron activations and applies adaptive masking to modulate their influence during generation. |
| Outcome: | The proposed framework detects context-aware neuron activations and applies adaptive masking to modulate their influence during generation. |
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)
Copied to clipboard
Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin
| Challenge: | Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation" |
| Approach: | They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations. |
| Outcome: | The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC) |
ModelCitizens: Representing Community Voices in Online Safety (2025.emnlp-main)
Copied to clipboard
Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel
| Challenge: | Existing toxic language detection models are trained on annotations that collapse diverse perspectives into a single ground truth. |
| Approach: | They propose to augment social media posts with conversational scenarios to reflect the impact of conversational context on toxicity. |
| Outcome: | The proposed model outperforms existing models on social media with conversational scenarios. |
Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogs (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior studies have demonstrated that Large Language Models (LLMs) are capable of performing grounding acts such as requesting clarification or producing acknowledgments, yet relatively little work has investigated how common ground can be explicitly represented and stored for later use. |
| Approach: | They propose to use relational references to represent common ground in situated dialogues and propose to improve both the establishment of common ground and its subsequent use in the conversation. |
| Outcome: | The proposed models can establish and exploit common ground in situated dialogues and improve its subsequent use. |
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents (2026.findings-acl)
Copied to clipboard
Jiliang Hu, Wenfu Wang, Zuchao Li, Chenxing Li, Yiyang Zhao, Hanzhao Li, Liqiang Zhang, Meng Yu, Dong Yu
| Challenge: | despite advances in multimodal conversational systems, current benchmarks lack comprehensive evaluation across key dimensions. |
| Approach: | They propose a Chinese benchmark built exclusively on real human speech to fill this gap . they assess LALMs across three complementary axes: instruction following, knowledge understanding, robustness . |
| Outcome: | VCB Bench assesses LALMs across three complementary axes: instruction following, knowledge understanding, and robustness . VCBM Bench provides reproducible and fine-grained framework for Chinese voice chat bots . results show significant performance disparities and offer tangible insights for future improvements . |
Query-Focused Individual Simulation with Progressive Persona Completion (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to simulating individual responses from persona information assume rich persona profiles, which are often unavailable in practice. |
| Approach: | They propose a query-focused individual simulation where relevant persona information is identified and requested on demand for each query. |
| Outcome: | Experiments on two dialogue datasets show that the proposed method achieves comparable performance to approaches that rely on rich persona information extracted from dialogue history. |
FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments (2026.findings-acl)
Copied to clipboard
Amir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay, Gaowen Liu, Ali Payani, Jayanth Srinivasa, Chitta Baral
| Challenge: | Large Language Models are being increasingly deployed as decision-making core of autonomous agents . however, in conversational benchmarks, these agents fail due to the cascading effects of incorrect decision- making . |
| Approach: | They propose a framework that analyzes failure trajectories from baseline agents to identify most prevalent errors. |
| Outcome: | Experiments show that the framework improves performance over open-source LLMs . the framework can be used to build reliable, multi-turn tool-use agents . |
EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing medical dialogue corpora are largely dyadic or lack multi-party workflow and annotations needed for this setting. |
| Approach: | They propose an ePCR-grounded, topic-flow-based multi-agent generation pipeline that iteratively plans, generates, and self-refines dialogues with rule-based factual and topic flow checks. |
| Outcome: | The proposed pipeline yields a dataset of 4,414 synthetic multi-speaker EMS conversations annotated with 43 diagnoses, speaker roles, and turn-level topics. |
Reading Between the Lines: The One-Sided Conversation Problem (2026.findings-acl)
Copied to clipboard
| Challenge: | In many real-world scenarios, only one side of a conversation is available for processing. |
| Approach: | They propose a one-sided conversation problem to reconstruct the missing speaker's turns and generate faithful summaries from one-side transcripts. |
| Outcome: | The proposed model improves reconstructions with prompting, but smaller models require fine tuning. |
PsyChain: A Collaborative Chain-of-Agents Framework for Generating Personalized and Professional Counseling Dialogues (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing psychological counseling datasets suffer from monolithic client personas, insufficient therapeutic depth, and a lack of process controllability. |
| Approach: | They propose a framework that evolves static counseling corpora into high-fidelity dialogues . they use a Client Profiler that pairs life scenarios with psychological personality archetypes based on client personality and stage progression . |
| Outcome: | The proposed framework achieves 61-91% win rates against domain-specific baselines in pairwise evaluation and the highest average score in human evaluation, indicating potential for real-world counseling. |
ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)
Copied to clipboard
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
| Challenge: | Existing autoregressive models for dialogue generation suffer from high latency and stability issues. |
| Approach: | They propose a non-autoregressive (NAR) zero-shot spoken dialogue generation model based on flow-matching. |
| Outcome: | The proposed model outperforms existing models in speech generation due to poor speech intelligibility and turn-taking precision. |
Wait! There’s a Way Out: A Decision Mechanism for Forecasting Conversational Derailment (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches make decision to "trigger" based on the estimated likelihood of derailment given the preceding utterances, implicitly assuming that the conversation’s future trajectory is fixed. |
| Approach: | They propose a method for decoupling the decision to trigger from derailment likelihood estimation. |
| Outcome: | The proposed method is inspired by the first human baseline on this task, which shows that humans achieve dramatically lower false positive rates by selectively deferring their decision to trigger when they anticipate that tension is likely to subside. |
Locate and Explain: Joint Multimodal Emotion Cause Extraction and Summarization in Conversation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on utterance-level emotion cause extraction and multimodal emotion cause generation, resulting in subjective and inconsistent annotations. |
| Approach: | They propose a task that extracts emotion cause utterances and generates cause summaries . they propose utterrance-level emotion cause extraction and multimodal emotion cause generation tasks . |
| Outcome: | The proposed task extracts emotion cause utterances and generates cause summaries . the proposed task establishes strong benchmark results for the proposed project . |
DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions . |
| Approach: | They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework. |
| Outcome: | The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution. |
DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference (2026.acl-long)
Copied to clipboard
Parisa Rabbani, Priyam Sahoo, Ruben Mathew, Aishee Mondal, Harshita Ketharaman, Nimet Beyza Bozdag, Dilek Hakkani-Tür
| Challenge: | a single model can shift toward disagreement (skepticism) on graduate-level science and toward agreement (deference) on social judgment. |
| Approach: | They propose a framework to detect and mitigat framing-induced judgment shifts . they propose 'DialDefer' framework to help model disagreements and disagreements based on attribution . |
| Outcome: | The proposed framework detects and mitigates dialogic deference shifts in LLMs . human-vs-LLM attribution drives the largest shifts (17.7 pp swing) |