Papers with conversational agents
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (N18-2)
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| Challenge: | NAACL HLT 2018 is the biggest NAAPL conference to date . this year's conference highlights the vibrancy and vitality of the field . |
| Approach: | a new review form and an opportunity for authors to review the reviewers were introduced at this year's conference . the test-of-time awards are named in memory of Aravind Joshi, who died this year . |
| Outcome: | the biggest NAACL conference to date features a new review form and the Test-of-Time awards . the industrial track features papers that focus on scalable, interpretable, reliable and customer facing methods for industrial applications . |
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (N18-1)
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| Challenge: | NAACL HLT 2018 is the biggest NAAPL conference to date . this year's conference highlights the vibrancy and vitality of the field . |
| Approach: | a new review form and an opportunity for authors to review the reviewers were introduced at this year's conference . the test-of-time awards are named in memory of Aravind Joshi, who died this year . |
| Outcome: | the biggest NAACL conference to date features a new review form and the Test-of-Time awards . the industrial track features papers that focus on scalable, interpretable, reliable and customer facing methods for industrial applications . |
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study (P19-1)
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| Challenge: | Neural generative models are becoming more popular when building conversational agents. |
| Approach: | They propose to study the sensitivity of neural dialog models to unnatural perturbations . they experiment with 10 different types of perturbations on 4 multi-turn dialog datasets . |
| Outcome: | The proposed model is sensitive to unnatural changes or perturbations on 4 multi-turn dialog datasets. |
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
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| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
| Outcome: | The proposed approach can be quickly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users. |
A Unified Framework for Emotion Identification and Generation in Dialogues (2023.eacl-srw)
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| Challenge: | Social chatbots have gained immense popularity and can be used to develop and promote social chatbot applications. |
| Approach: | They propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion. |
| Outcome: | The proposed framework outperforms current state-of-the-art models with classification and generation loss. |
Guiding Variational Response Generator to Exploit Persona (2020.acl-main)
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Bowen Wu, MengYuan Li, Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun Wang
| Challenge: | Neural Response Generators (NRGs) use persona information of users to perform personalized conversations . current studies focus on incorporating explicit meta-data of user profiles or character descriptions to generate persona-aware responses. |
| Approach: | They propose to use persona information of users in Neural Response Generators to perform personalized conversations. |
| Outcome: | The proposed method improves persona-aware response generation and the metrics are reasonable to evaluate them. |
Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q&A Corpus (D19-51)
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| Challenge: | Existing studies on DA classification in general contexts have not addressed this problem. |
| Approach: | They constructed a text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines. |
| Outcome: | The text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines was constructed using a tagset for DA classification . the corpus was composed dominantly of single-label posts, with 34% of the corpuse having multiple intent tags. |
Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee (2022.findings-aacl)
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| 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. |
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation (N18-1)
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| Challenge: | a recent study has shown that natural language generators produce utterances with humanlike coherence and naturalness for many different kinds of content. |
| Approach: | They propose to use a neural language generator to generate a syntactically and semantically correct utterance from a given MR. |
| Outcome: | The proposed model outperforms state-of-the-art models on restaurant, TV and laptop datasets. |
ScopeIt: Scoping Task Relevant Sentences in Documents (2020.coling-industry)
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| Challenge: | a problem faced by conversational agents working with large documents is the frequent presence of information that is irrelevant to the agent. |
| Approach: | They propose a neural model for scoping relevant information from a large document . they show that the model performs better with emails than existing baselines . |
| Outcome: | The proposed model improves intent detection and entity extraction tasks without drop in recall. |
DeepPavlov: Open-Source Library for Dialogue Systems (P18-4)
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Mikhail Burtsev, Alexander Seliverstov, Rafael Airapetyan, Mikhail Arkhipov, Dilyara Baymurzina, Nickolay Bushkov, Olga Gureenkova, Taras Khakhulin, Yuri Kuratov, Denis Kuznetsov, Alexey Litinsky, Varvara Logacheva, Alexey Lymar, Valentin Malykh, Maxim Petrov, Vadim Polulyakh, Leonid Pugachev, Alexey Sorokin, Maria Vikhreva, Marat Zaynutdinov
| Challenge: | open-source library DeepPavlov is designed for rapid development of dialogue systems. |
| Approach: | open-source library DeepPavlov is tailored for development of conversational agents . the library prioritizes efficiency, modularity and extensibility with the goal to make it easier to develop dialogue systems from scratch . |
| Outcome: | the open-source library DeepPavlov is designed for rapid development of dialogue systems . it supports modular as well as end-to-end approaches to implementation of conversational agents . |
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology (2024.findings-eacl)
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| Challenge: | Scholarly communication in the digital age is facing significant challenges due to the overwhelming volume of publications. |
| Approach: | They propose to use Wikipedia infoboxes and structured Amazon product descriptions to create structured scholarly contribution summaries using text generation capabilities of LLMs. |
| Outcome: | The proposed model can be applied to complex IE tasks within terse domains like Science with 1000x fewer parameters than the state-of-the-art GPT-davinci. |
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation (2024.eacl-short)
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| Challenge: | Generating natural language text from graph-structured data is essential for conversational information seeking. |
| Approach: | They conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples using a WebNLG dataset. |
| Outcome: | The proposed models improve their ability to generate natural language text from semantic triples using few-shot prompting, post-processing, and efficient fine-tuning techniques. |
PAL: Persona-Augmented Emotional Support Conversation Generation (2023.findings-acl)
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| Challenge: | Recent work has demonstrated the effectiveness of dialogue models in providing emotional support due to the lack of human resources for mental health support. |
| Approach: | They propose a framework for dynamically inferring and modeling seekers’ persona from the conversation history and a model that leverages persona information to provide personalized emotional support. |
| Outcome: | The proposed model outperforms baseline models on the studied benchmark. |
Chandler: An Explainable Sarcastic Response Generator (2021.emnlp-demo)
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| Challenge: | sarcasm generators assume intended meaning is opposite of literal meaning . sarcastically generated responses are more specific and coherent to input . |
| Approach: | They propose a system that generates sarcastic responses to a given utterance . they ground their generation process on a formal theory that unambiguously differentiates . |
| Outcome: | The proposed system generates sarcastic responses to a given utterance. |
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning (2020.coling-main)
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| Challenge: | Using a multi-task learning framework, we train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Approach: | They propose a multi-task learning framework to train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Outcome: | The proposed model outperforms individual tasks and delivers competitive performance. |
DeepPavlov Dream: Platform for Building Generative AI Assistants (2023.acl-demo)
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Diliara Zharikova, Daniel Kornev, Fedor Ignatov, Maxim Talimanchuk, Dmitry Evseev, Ksenya Petukhova, Veronika Smilga, Dmitry Karpov, Yana Shishkina, Dmitry Kosenko, Mikhail Burtsev
| Challenge: | open-source DeepPavlov Dream Platform is designed for development of complex dialog systems . platform supports modular approach to implementation of conversational agents . |
| Approach: | open-source DeepPavlov Dream Platform is designed for development of complex dialog systems . platform includes a conversational orchestrator called DeepPvlov Agent to coordinate asynchronous dialog pipeline . |
| Outcome: | The open-source DeepPavlov Dream Platform is designed for development of complex dialog systems like Generative AI Assistants. |
FedPerC: Federated Learning for Language Generation with Personal and Context Preference Embeddings (2023.findings-eacl)
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| Challenge: | federated learning is a decentralized learning paradigm that assumes no access to a large labeled dataset and instead leverages averaged parameter updates across all users of the system. |
| Approach: | They propose a method to personalize federated learning with personal embeddings and shared context embeddables. |
| Outcome: | The proposed approach achieves 50% improvement in test-time perplexity using 0.001% of the memory required by baseline approaches and greater sample- and compute-efficiency. |
Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
| Approach: | They propose an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. |
| Outcome: | The proposed architecture improves the next utterance prediction in chit-chat type of generative dialogue models by incorporating external knowledge from Wikipedia summaries and the NELL knowledge base. |
Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)
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| Challenge: | a new method to extract user attributes from dialogues is needed to improve user understanding. |
| Approach: | They propose to leverage dialogues with conversational agents to automatically extract user attributes from dialogues. |
| Outcome: | The proposed model surpasses retrieval and generation baselines on human evaluation. |
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues (2022.coling-1)
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| Challenge: | a recent work on argument mining has focused on parsing monologues, while neglecting dialogues. |
| Approach: | They propose an end-to-end argument parser that constructs argument graphs from dialogues . they use extensive pre-training and curriculum learning to train AM . |
| Outcome: | The proposed system performs all sub-tasks of AM and achieves significant improvements . it is compared to existing systems and validated through human evaluation . |
An Annotation Approach for Social and Referential Gaze in Dialogue (2020.lrec-1)
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| Challenge: | Existing studies on eye gaze information focus on social functions and how it is used in reference resolution. |
| Approach: | They propose an approach for annotating eye gaze considering its social and referential functions in multi-modal dialogue. |
| Outcome: | The proposed annotation scheme is based on eye gaze behavior cues in human-human dialogues. |
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models (N19-1)
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| Challenge: | Existing approaches to define action spaces for conversational agents have limitations . end-to-end dialog systems can handle complex domains with limited action space . |
| Approach: | They propose a latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods to induce its own action space from the data. |
| Outcome: | The proposed framework achieves better performance than word-level policy gradient methods on DealOrNoDeal and MultiWoz dialogs. |
What Speakers really Mean when they Ask Questions: Classification of Intentions with a Supervised Approach (2020.lrec-1)
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| Challenge: | Existing work on hidden intentions of speakers in questions during meals is based on written or oral data, which are less easy to interpret. |
| Approach: | They propose a typology of hidden intentions in questions asked during meals . they implement an automatic classification model based on annotated data and selected linguistic features. |
| Outcome: | The proposed model is based on annotated data and features and evaluates its performance. |
Personality Editing for Language Models through Adjusting Self-Referential Queries (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) are integral to conversational agents and content creation, but they lack robustness and require large-scale training data to achieve significant improvements in personality alignment. |
| Approach: | They propose a method that introduces adjustment queries where self-referential statements grounded in psychological constructs are treated analogously to factual knowledge to enable direct editing of personality-related responses. |
| Outcome: | The proposed method improves personality alignment across personality dimensions and requires only 12 editing samples to achieve significant improvements. |
EgoSpeak: Learning When to Speak for Egocentric Conversational Agents in the Wild (2025.findings-naacl)
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Junhyeok Kim, Min Soo Kim, Jiwan Chung, Jungbin Cho, Jisoo Kim, Sungwoong Kim, Gyeongbo Sim, Youngjae Yu
| Challenge: | EgoSpeak predicts when an agent should begin speaking based on egocentric streaming video. |
| Approach: | They propose a framework for real-time speech initiation prediction in egocentric streaming video by modeling the conversation from the camera wearer's first-person perspective. |
| Outcome: | The proposed framework outperforms random and silence-based baselines in real time and highlights the importance of multimodal input and context length in effectively deciding when to speak. |
Robust Conversational Agents against Imperceptible Toxicity Triggers (2022.naacl-main)
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| Challenge: | Existing work to generate adversarial attacks is costly and not scalable . despite the abundance of research in this area, little attention has been given to adversarials . |
| Approach: | They propose an adversarial attack mechanism that mitigates toxic language generation . they propose a defense mechanism that is scalable and can be generalized . |
| Outcome: | The proposed defense is effective at avoiding toxic language generation even against imperceptible toxicity triggers while preserving conversational flow. |
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)
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| 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. |
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario (2025.coling-main)
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| Challenge: | Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed. |
| Approach: | They propose to use textual data augmentation (DA) to generate new sentences for text classification in a limited data setting. |
| Outcome: | The proposed methods perform better on small data settings and on large datasets, but they are not as effective on large data sets. |
R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression (2025.findings-acl)
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| Challenge: | Existing memory solutions that store information via parameters struggle with reliable retrieval. |
| Approach: | They propose a memory network that optimizes both information Retention and Retrieval through Reversible context compression. |
| Outcome: | The proposed memory network outperforms conventional memory modules in long-horizon interaction tasks like conversational agents and achieves state-of-the-art performance in language modeling and retrieval-augmented generation tasks. |
GLoHBCD: A Naturalistic German Dataset for Language of Health Behaviour Change on Online Support Forums (2022.lrec-1)
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| Challenge: | Existing motivational interviewing methods lack the deep understanding of user utterances that is essential to the spirit of motivational interviews. |
| Approach: | They propose to use a German dataset of naturalistic language around health behaviour change to examine the motivational state of the user. |
| Outcome: | The proposed dataset of naturalistic language around health behaviour change is based on a weight loss forum in germany and is evaluated using theoretically grounded motivational interviewing categories. |
One Agent To Rule Them All: Towards Multi-agent Conversational AI (2022.findings-acl)
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Christopher Clarke, Joseph Peper, Karthik Krishnamurthy, Walter Talamonti, Kevin Leach, Walter Lasecki, Yiping Kang, Lingjia Tang, Jason Mars
| Challenge: | Increasing volume of conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. |
| Approach: | They propose a task BBAI: Black-Box Agent Integration that integrates multiple black-box CAs at scale. |
| Outcome: | The proposed system outperforms existing benchmarks in the BBAI: Black-Box Agent Integration task. |
SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models (2024.findings-naacl)
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| Challenge: | SUQL is a conversational language that supports the generality of hybrid data access for large knowledge corpora. |
| Approach: | They propose a conversational agent that supports the full generality of hybrid data access for large knowledge corpora using SUQL. |
| Outcome: | The proposed language can handle hybrid data sources. |
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents (2021.naacl-main)
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| Challenge: | End-to-end deep learning methods that focus on user satisfaction are challenging due to the required annotation costs and turnaround times. |
| Approach: | They propose a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. |
| Outcome: | The proposed approach reduces the required number of annotations while improving generalization on unseen skills. |
SOLID: Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking Dialogs (2025.findings-naacl)
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Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi, Amin Abolghasemi, Evangelos Kanoulas, Suzan Verberne
| Challenge: | Existing methods for intent prediction rely on human feedback and are tailored to structured intents. |
| Approach: | They propose a method that generates dialogs turn-by-turn using self-seeding and multi-intent self-instructing strategies. |
| Outcome: | The proposed methods generate dialogs turn-by-turn using self-seeding and multi-intent self-instructing strategies. |
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (N19-1)
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| Challenge: | Previous work focused on generating semantically similar paraphrases without considering diversity. |
| Approach: | They propose a method to obtain highly diverse paraphrases without compromising on paraphrasing quality by using monotone submodular function maximization. |
| Outcome: | The proposed method is effective on multiple tasks such as intent classification and paraphrase recognition. |
Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic? (2022.coling-1)
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| Challenge: | Language Technology (LT) has been used in the COVID-19 pandemic, but only in a handful of languages. |
| Approach: | They propose to use conversational agents for information dissemination and basic diagnosis in 15 Asian and African languages with varying resource-availability to test their knowledge of LT. |
| Outcome: | The proposed research confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help prioritize research and investment strategies in LT for healthcare. |
Large Dataset and Language Model Fun-Tuning for Humor Recognition (P19-1)
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| Challenge: | Humor recognition datasets contain only English texts and focus on puns. |
| Approach: | They collected a dataset of jokes and funny dialogues in Russian and complemented them carefully with unfunny texts with similar lexical properties. |
| Outcome: | The proposed method is based on the universal language model finetuning and has an F1 score of 0.91 on a test set. |
Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games (2023.findings-acl)
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Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James Rehg, Diyi Yang
| Challenge: | Existing studies on persuasive behavior modeling focus on textual dialogues . a multimodal dataset is available for persuasion modeling . |
| Approach: | They propose a multimodal dataset for modeling persuasive behaviors using visual signals. |
| Outcome: | The proposed dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting and 26,647 utterance level annotations of persuasion strategy and game level annotation of deduction game outcomes. |
Corpus Design for Studying Linguistic Nudges in Human-Computer Spoken Interactions (2022.lrec-1)
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Natalia Kalashnikova, Serge Pajak, Fabrice Le Guel, Ioana Vasilescu, Gemma Serrano, Laurence Devillers
| Challenge: | linguistic nudges can influence people to the same degree as a human agent, according to Thaler and Sunstein (2008). |
| Approach: | They propose to use a corpus design method to compare influence between linguistic nudges with positive or negative influences and three conversational agents: robot, smart speaker, and human. |
| Outcome: | The results show that linguistic nudges can influence participants to the same degree as human agents. |
IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback (2026.acl-long)
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| Challenge: | Existing approaches to optimize conversational agents often rely on explicit preference pairs and expert evaluations. |
| Approach: | They propose a conversational agent framework that leverages the structured dependency between agent responses and user reactions to extract implicit feedback. |
| Outcome: | The proposed framework improves on MT-Bench-101, WildBench, and FB-Bech, and shows that mining implicit feedback supports better multi-turn alignment under evolving user preferences. |
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics (2025.findings-naacl)
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Seungbeen Lee, Seungwon Lim, Seungju Han, Giyeong Oh, Hyungjoo Chae, Jiwan Chung, Minju Kim, Beong-woo Kwak, Yeonsoo Lee, Dongha Lee, Jinyoung Yeo, Youngjae Yu
| Challenge: | Recent advances in Large Language Models (LLMs) have led to their adaptation as conversational agents. |
| Approach: | They propose a new benchmark that uses 8K multi-choice questions to assess the personality of Large Language Models. |
| Outcome: | The proposed personality test outperforms existing personality tests for LLMs in reliability and validity. |
Main Predicate and Their Arguments as Explanation Signals For Intent Classification (2025.naacl-long)
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| Challenge: | Intent classification is crucial for conversational agents, and deep learning models perform well in this area due to the lack of suitable benchmark data. |
| Approach: | They propose a technique to augment text samples from intent classification datasets with word-level explanations by marking main predicates and their arguments as explanation signals. |
| Outcome: | The proposed method augments text samples from intent classification datasets with word-level explanations. |
From Trust to Compromise: Outcome-Verified LLM Phishing Simulation and Real-Time Defense (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) excel as conversational agents, but existing simulators focus on PII requests within the chat. |
| Approach: | They propose a large language model that generates human-like language and maintains conversational context to automate social engineering attacks. |
| Outcome: | The proposed model improves dialogue-level detection over a real-time baseline. |
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules (2021.emnlp-main)
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| Challenge: | Currently, conversational agents lack commonsense reasoning, preventing them from engaging in rich conversations with humans. |
| Approach: | They propose a commonsense reasoning system that uncovers unstated presumptions from user commands satisfying a general template of if-(state), then-(action), because-(goal) They propose to use a transformer-based generative commons sense knowledge base as its source of background knowledge to extract multi-hop reasoning chains from the neural KB. |
| Outcome: | The proposed model achieves a 35% higher success rate than existing methods with human users. |
Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model (2025.acl-long)
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Emre Can Acikgoz, Jeremiah Greer, Akul Datta, Ze Yang, William Zeng, Oussama Elachqar, Emmanouil Koukoumidis, Dilek Hakkani-Tür, Gokhan Tur
| Challenge: | Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA) current approaches excel in one domain but underperform in the other. |
| Approach: | They propose a unified approach that integrates both conversational and agentic capabilities. |
| Outcome: | The proposed model outperforms top domain-specific models across three benchmarks. |
Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems (2023.findings-emnlp)
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| Challenge: | Existing conversational recommender systems focus on a single-shot approach to understand user preferences and provide recommendations. |
| Approach: | They propose a problem space for conversational agents that aim to provide both product recommendations and educational value through mixed-type mixed-initiative dialog. |
| Outcome: | The proposed framework can simulate salesbot and shopperbot agents and provide both product recommendations and educational value through mixed-type mixed-initiative dialog. |
Grounding Task Assistance with Multimodal Cues from a Single Demonstration (2025.findings-acl)
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Gabriel Herbert Sarch, Balasaravanan Thoravi Kumaravel, Sahithya Ravi, Vibhav Vineet, Andrew D Wilson
| Challenge: | RGB video often fails to capture fine-grained contextual cues such as intent, safety-critical environmental factors, and subtle preferences embedded in human behavior. |
| Approach: | They propose a framework that integrates eye gaze and speech cues to improve conversational agents for task assistance by integrating eye gaze with speech cuests. |
| Outcome: | The proposed framework captures fine-grained intent and user-specific cues, enabling richer contextual grounding for visual question answering. |
YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents (2026.acl-long)
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| 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. |
Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark (2023.emnlp-main)
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| Challenge: | Existing benchmarks of social language are lacking for large language models. |
| Approach: | They propose a new benchmark that measures how well large language models understand social language by grouping 58 tasks into five categories: humor & sarcasm, offensiveness, sentiment & emotion, and trustworthiness. |
| Outcome: | The proposed model performs well at 58 tasks that are divided into five categories: humor & sarcasm, offensiveness, sentiment & emotion, and trustworthiness. |
Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions (2025.findings-acl)
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Taedong Yun, Eric Yang, Mustafa Safdari, Jong Ha Lee, Vaishnavi Vinod Kumar, S. Sara Mahdavi, Jonathan Amar, Derek Peyton, Reut Aharony, Andreas Michaelides PhD, Logan Douglas Schneider, Isaac Galatzer-Levy, Yugang Jia, John Canny, Arthur Gretton, Maja Mataric
| Challenge: | Structured data is generated grounded in health and lifestyle factors and full profiles of synthetic users are developed conditioned on the structured data. |
| Approach: | They propose an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle coaching. |
| Outcome: | The proposed framework is validated in the domains of sleep and diabetes coaching using two independently-developed agents for sleep and diabetic coaching as case studies. |
RedDust: a Large Reusable Dataset of Reddit User Traits (2020.lrec-1)
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| Challenge: | Social media is a rich source of assertions about personal traits, but identifying personal traits from implicit assertions is difficult because of the users’ highly varied vocabulary and expressions. |
| Approach: | They propose to build a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age, and gender. |
| Outcome: | The proposed resource is the first annotated language resource about Reddit users at large scale. |
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used as conversational agents. |
| Approach: | They construct a dataset of knowledge-intensive writing instructions to evaluate LLMs' ability to follow user instructions. |
| Outcome: | The proposed model fails to integrate new information into an existing answer and perform precise and unambiguous edits. |
Construction of Responsive Utterance Corpus for Attentive Listening Response Production (2022.lrec-1)
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| Challenge: | In Japan, the number of single-person households is increasing, reducing opportunities for people to narrate. |
| Approach: | They propose to collect 148,962 responsive utterances by listeners and annotate existing narrative speech with responsive . they also propose to use robots and smart speakers to listen to narratives . |
| Outcome: | The proposed method can be used to annotate existing narrative speech with responsive utterances. |
Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs (2023.findings-emnlp)
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Young-Suk Lee, Md Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Astudillo
| Challenge: | Empirical studies with different instruction-tuned LMs show that our proposed method yields higher-quality instruction tuning data than Self-Instruct. |
| Approach: | They propose to use in-context learning techniques to train strong conversational agents . they propose to categorize and simplify ICL templates to make prompt learning easier . |
| Outcome: | Empirical results show that the proposed method yields higher-quality instruction tuning data than Self-Instruct and improves performance of both vanilla and instruction-tuned LMs. |
KEEP CHATTING! An Attractive Dataset for Continuous Conversation Agents (2024.findings-acl)
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| Challenge: | Existing works about persona dialogue such as PersonaChat have greatly facilitated the chatbot with configurable and persistent personalities. |
| Approach: | They propose to collect a dataset called ContinuousChat and rewrite it in style-specific ways to increase users' willingness to continue chatting. |
| Outcome: | The proposed model increases users' willingness to continue talking to the chatbot by increasing their personas to detailed-personas through experiences, daily life, future plans, or interesting stories. |
A Survey of Ontology Expansion for Conversational Understanding (2024.emnlp-main)
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| Challenge: | Current methods for conversational understanding rely on static ontologies, limiting their ability to handle new and unforeseen user needs. |
| Approach: | They propose to review the state-of-the-art techniques in OnExp for conversational understanding and highlight emerging frontiers . they categorize existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. |
| Outcome: | The proposed methods highlight several emerging frontiers in OnExp to improve agent performance in real-world scenarios and discuss their corresponding challenges. |
On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts (2025.emnlp-main)
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| Challenge: | Language models (LMs) are increasingly used as conversational agents because of their pragmatic reasoning abilities. |
| Approach: | They propose an evaluation framework derived from *Wavelength*, a popular communication game where a speaker and a listener communicate about a broad range of concepts in a granular manner. |
| Outcome: | The proposed evaluation framework outperforms direct and Chain-of-Thought (CoT) prompting on language comprehension and language production tasks. |
PsyDial: A Large-scale Long-term Conversational Dataset for Mental Health Support (2025.acl-long)
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| Challenge: | Existing models for mental health counseling use a privacy-preserving data reconstruction method to reconstruct client-counselor dialogues without removing personally identifiable information due to privacy concerns. |
| Approach: | They propose a privacy-preserving data reconstruction method that reconstructs real-world client-counselor dialogues while mitigating privacy concerns. |
| Outcome: | The proposed method reduces privacy risks while maintaining dialogue diversity and conversational exchange while maintaining conversational diversity. |
A Self-Evolving LLM Agent Framework for Role-Based Norm Compliance in Healthcare (2026.findings-acl)
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| Challenge: | Existing systems treat roles as static prompts and rely on one-shot safety filters . a self-evolving LLM agent is proposed that learns from role-based social experience . |
| Approach: | They propose a self-evolving LLM agent that learns from role-based social experience and explicitly models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
| Outcome: | The proposed agent learns from role-based social experience and models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets (2025.acl-long)
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| Challenge: | Existing LLMs suffer from hallucination, following instructions with conditional logic, and integrating knowledge from different sources. |
| Approach: | They propose a programmable framework for creating knowledge-intensive task-oriented conversational agents that handle involved interactions and answer complex queries. |
| Outcome: | The proposed framework outperforms SOTA methods on complex logic dialogue datasets by up to 20.5%. |
A Similarity Measure for Comparing Conversational Dynamics (2025.findings-emnlp)
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| Challenge: | Qualities of a conversation are dependent on how interactions combine to form a “shape” of the conversation. |
| Approach: | They propose a similarity measure to capture differences in conversation dynamics and assess its sensitivity to the topic of the conversation. |
| Outcome: | The proposed measure captures differences in conversation dynamics and assesses its sensitivity to the topic of the conversation. |
Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling (2025.acl-long)
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| Challenge: | Recent LLMs are known to hallucinate, producing responses that seem plausible but are factually incorrect. |
| Approach: | They propose an accountability model for LLM-based task-oriented dialogue agents to address user overreliance via friction turns in cases of model uncertainty and errors associated with dialogue state tracking (DST). |
| Outcome: | The proposed model improves joint goal accuracy (JGA) of DST output by 3% on two established benchmarks. |
Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models (2026.acl-long)
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| Challenge: | Large language models are increasingly used as conversational agents that adopt personas and role-play characters at user request. |
| Approach: | They propose to examine how persona agreeableness influences sycophancy across 13 small, open-weight language models ranging from 0.6B to 20B parameters. |
| Outcome: | The proposed model consists of 275 personas and exposes them to 4,950 sycophancy-eliciting prompts spanning 33 topic categories. |
Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation (2024.lrec-main)
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| Challenge: | Current task-oriented dialogue systems are trained on a single conversational style and do not account for the diversity of styles encountered when interacting with different users. |
| Approach: | They propose a method for generating multi-style conversations using a multi-language dataset that is available in a conversational domain. |
| Outcome: | The proposed model can be used in the development of conversational agents . it assumes the availability of a conversational domain and leverages the generative capabilities of large language models. |
Can LLM Agents Maintain a Persona in Discourse? (2025.emnlp-main)
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Pranav Bhandari, Nicolas Fay, Michael J Wise, Amitava Datta, Stephanie Meek, Usman Naseem, Mehwish Nasim
| Challenge: | Large language models are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions. |
| Approach: | They propose to use two conversation agents to generate a discourse with an assigned personality from the OCEAN framework and then use multiple judge agents to infer original traits. |
| Outcome: | The proposed model is based on two conversation agents with a personality assigned from the OCEAN framework and then multiple judge agents to infer the original traits assigned. |
HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)
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| Challenge: | Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval. |
| Approach: | They propose a hypergraph-based hierarchical memory architecture that explicitly models high-order associations using hyperedges. |
| Outcome: | Experiments show that HyperMem achieves state-of-the-art performance with 92.73% accuracy for long-term conversations. |