Papers with dialogue system
One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems (2023.acl-long)
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| Challenge: | Recent studies have found that Task-oriented Dialogue systems can be more suitable for human users. |
| Approach: | They propose a framework to optimize ToD systems by leveraging Multiple User SimulaTors. |
| Outcome: | The proposed framework improves performance on multiWOZ with human evaluations and automatic evaluations. |
Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions (2022.acl-srw)
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| Challenge: | a human would recognize the emotion of an interlocutor and respond with an appropriate emotion, such as empathy and comfort. |
| Approach: | They propose to build a dialogue corpus annotated with two kinds of emotions . they collect tweets and annotate them with the emotion they put into the utterance . |
| Outcome: | The proposed method shows that it is difficult to recognize experienced emotions and multitask learning is effective. |
Multi-party Multimodal Conversations Between Patients, Their Companions, and a Social Robot in a Hospital Memory Clinic (2024.eacl-demo)
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Angus Addlesee, Neeraj Cherakara, Nivan Nelson, Daniel Hernandez Garcia, Nancie Gunson, Weronika Sieińska, Christian Dondrup, Oliver Lemon
| Challenge: | a new spoken dialogue system is being developed for hospitals and hospitals to enable multi-party interactions . a social robot can be used to have multi-part conversations with patients and their companions . |
| Approach: | They describe a spoken dialogue system that allows patients to have multi-party conversations with their companions . they use speech and video input to generate both speech and gestures - arm, head, and eye movements . |
| Outcome: | The proposed system generates human-like clarification requests when the patient pauses mid-utterance, answers in-domain questions, and responds appropriately to out-of-domain requests. |
Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue (2025.findings-naacl)
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| Challenge: | Recent efforts to improve depression-diagnosis-oriented dialogue systems ignore the Stigma preventing users from open conversations about their struggles. |
| Approach: | They propose a method to promote a sense of unobtrusiveness within the dialogue system and assessing depression disorder by probing symptoms. |
| Outcome: | The proposed method improves on baselines including unobtrusiveness evaluation of dialogue content and diagnostic accuracy. |
Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation (2022.naacl-srw)
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| Challenge: | Existing dialogue systems that generate fluent responses are difficult to evaluate due to the one-to-many nature of dialogue, which means the existence of multiple appropriate responses is not appropriate. |
| Approach: | They propose a generator-evaluator model that evaluates multiple responses generated by a response generator and selects the best response by an evaluators. |
| Outcome: | The proposed model is compared with a baseline system and its outputs were judged to be better than the baseline system. |
Building a Personalized Dialogue System with Prompt-Tuning (2022.naacl-srw)
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| Challenge: | We build a dialogue system that can respond based on a given character setting (persona) this method is not suitable because the more persona information is added, the longer the input text becomes. |
| Approach: | They propose to use prompt-tuning to build a dialogue system that responds based on a persona . they conduct automatic and manual evaluations on English and Japanese . |
| Outcome: | The proposed method can build a dialogue system with more natural responses with less computational resources than fine-tuning. |
Transferable Dialogue Systems and User Simulators (2021.acl-long)
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| Challenge: | a lack of training data is limiting the development of dialogue systems . we develop a framework for creating dialogue data through self-play between agents . |
| Approach: | They propose a framework that can incorporate new dialogue scenarios through self-play between two agents. |
| Outcome: | The proposed framework is highly effective in bootstrapping the performance of two agents in transfer learning. |
Samvaadhana: A Telugu Dialogue System in Hospital Domain (D19-61)
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| Challenge: | a dialogue system for Hospital domain in Telugu is a resource-poor Dravidian language . the system handles various hospital and doctor related queries . |
| Approach: | They propose to model a dialogue system for Hospital domain in Telugu which is a resource-poor Dravidian language. |
| Outcome: | The proposed system achieves a high overall rating and a significantly accurate context-capturing method. |
Explicit Use of Topicality in Dialogue Response Generation (2022.naacl-srw)
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| 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 . |
Task-oriented Dialogue System for Automatic Diagnosis (P18-2)
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Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai
| Challenge: | Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another. |
| Approach: | They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. |
| Outcome: | The proposed system can collect additional symptoms from conversation and improve disease identification accuracy. |
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems (2022.findings-acl)
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| Challenge: | Existing systems that require extensive labor to process user requests are limited in their reasoning capabilities and require extensive manual effort to design. |
| Approach: | They propose a method that allows a transformer model to walk on a large-scale knowledge graph to generate responses by reasoning over differentiable knowledge graphs. |
| Outcome: | The proposed method allows a transformer model to walk on a large-scale knowledge graph to generate responses. |
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems (2021.findings-emnlp)
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| Challenge: | Existing studies on open-domain dialogue systems that allow free topics are challenging . however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems . |
| Approach: | They propose to use English knowledge to improve the performance of open-domain dialogue systems . they construct a Korean-English T5 language model and develop a knowledge-grounded Korean dialogue model . |
| Outcome: | The proposed model improves even when only English knowledge is given . the model is built with a pre-trained language model and a knowledge-grounded Korean dialogue model . |
SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support (2024.findings-emnlp)
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| Challenge: | Developing specialized dialogue systems for mental health support requires multi-turn conversation data . data privacy protection, time and cost involved in crowdsourcing are challenges . a new method for rewriting public single-turn dialogues into multi-turned ones is needed . |
| Approach: | They propose a single-turn to multi-turn inclusive language expansion technique that prompts ChatGPT to rewrite public single-turned dialogues into multi-turned ones. |
| Outcome: | The proposed method generates a large-scale, lifelike, and diverse dialogue dataset . it also develops SMILECHAT, a mental health chatbot . |
ADEA: An Argumentative Dialogue Dataset on Ethical Issues Concerning Future A.I. Applications (2024.lrec-main)
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| Challenge: | Introducing ADEA: a dataset that captures online dialogues and focuses on ethical issues related to future AI applications. |
| Approach: | They propose a German dataset that captures online dialogues on ethical issues . the dataset includes over 2800 labeled user utterances on four different topics . they use an argument graph as the system's knowledge base and an annotation scheme . |
| Outcome: | The proposed dataset includes over 2800 user utterances on four ethical topics . the aim is to improve knowledge about AI ethics topics through argumentative dialogues . |
Interpretation of Implicit Conditions in Database Search Dialogues (C18-1)
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| Challenge: | Existing attempts to extract information from user utterances in database search dialogues have failed . |
| Approach: | They propose to utilise information in user utterances that do not directly mention database fields for constructing database queries. |
| Outcome: | The proposed model performs better than the existing model on a real estate agent-customer dialogue. |
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 (2020.acl-main)
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| 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 . |
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (2022.coling-1)
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Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, Shweta Yadav
| Challenge: | Health coaching is cost-prohibitive due to its highly personalized nature. |
| Approach: | They propose to build a health coaching dialogue system that converses with patients . they propose to use simplified NLU and NLG frameworks and mechanism-conditioned empathetic response generation. |
| Outcome: | The proposed system generates more empathetic, fluent, and coherent responses . it outperforms the state-of-the-art in NLU tasks while requiring less annotations. |
Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems (2020.lrec-1)
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| Challenge: | Argumentative dialogue systems and chat bots require a database of arguments that matches their requirements. |
| Approach: | They propose a dialogue system that presents arguments by virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories. |
| Outcome: | The proposed system evaluates arguments retrieved by two state-of-the-art argument search engines and a system based on traditional web search. |
TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation (2024.acl-long)
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| Challenge: | Current methods for Continual Dialogue State Tracking (DST) struggle with catastrophic forgetting and knowledge transfer between tasks. |
| Approach: | They propose a framework for task skill localization and consolidation that enables effective knowledge transfer without relying on memory replay. |
| Outcome: | The proposed framework shows a 7.6% increase in Avg. JGA and 11% rise in BWT metrics over existing state-of-the-art methods. |
MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships (2020.lrec-1)
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| Challenge: | Existing datasets with emotion and relation labels for dialogues are limited. |
| Approach: | They use a Chinese dialogue dataset to annotate emotions and interpersonal relationships on each utterance. |
| Outcome: | The proposed dataset contains 25,548 utterances from 4,142 dialogues. |
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator (2022.findings-emnlp)
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| Challenge: | Task-oriented dialogue systems are drawing more attention in recent studies . current evaluation methods use annotated utterances in multi-turn dialogue sessions . |
| Approach: | They propose an interactive evaluation framework for task-oriented dialogue (TOD) they build a goal-oriented user simulator based on pre-trained models and use it to generate dialogues . |
| Outcome: | The proposed evaluation framework achieves 98% inform and success rates in the multi-woz dataset and the proposed scores measure the response quality besides the inform and successful rates. |
LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)
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Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Structured knowledge grounding (SKG) tasks are a key part of many NLP applications. |
| Approach: | They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format . |
| Outcome: | The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning. |
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)
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| Challenge: | A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections. |
| Approach: | They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system. |
| Outcome: | The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values . |
A Sequence-to-Sequence Approach to Dialogue State Tracking (2021.acl-long)
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| Challenge: | Existing methods for dialogue state tracking are still challenging, but they are improving . a new approach to dialogue state monitoring is proposed, called Seq2Seq-DU . |
| Approach: | They propose a new dialogue state tracking module that formalizes DST as a sequence-to-sequence problem. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets in different settings. |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
Assessing Dialogue Systems with Distribution Distances (2021.findings-acl)
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| Challenge: | Existing evaluation metrics focus on turnlevel quality, which is not well suited for open-end dialogue tasks. |
| Approach: | They propose to measure the performance of a dialogue system by computing the distributionwise distance between its generated conversations and real-world conversations. |
| Outcome: | The proposed metrics correlate better with human judgments than existing metrics on dialogue systems. |
Dialogue Scenario Collection of Persuasive Dialogue with Emotional Expressions via Crowdsourcing (L18-1)
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| Challenge: | Existing methods for data collection and annotation are costly and prevent launching new dialogue systems. |
| Approach: | They asked crowd workers to create persuasive dialogue systems using emotional expressions . they annotated emotional states and users' acceptance for system persuasion . |
| Outcome: | The proposed system has sufficient agreement even without training, the researchers found . the experiment showed that the collected data are comparable to real-world dialogue recording methods . |
KETOD: Knowledge-Enriched Task-Oriented Dialogue (2022.findings-naacl)
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| Challenge: | Existing studies treat task-oriented dialogue and chit-chat as separate domains . a new dataset is created to integrate both types of dialogue into a single system . |
| Approach: | They propose to integrate task-oriented dialogue and knowledge-grounded chit-chat into a single model by using a dataset. |
| Outcome: | The proposed models improve the performance of knowledge-enriched dialogues while maintaining a competitive task-oriented dialog performance. |
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users (2023.findings-emnlp)
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Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
| Challenge: | a dataset of task-oriented dialogues assume conversations between the agent and one user at a time . but multi-user task-orientated dialogues are richer, containing deliberation and deliberations . a novel task is proposed to rewrite a task-focused query that retains only task-relevant information . |
| Approach: | They propose to rewrite a task-oriented chat between two users as a concise task-orientated query that retains only task-relevant information and is directly consumable by the dialogue system. |
| Outcome: | The proposed method surpasses existing models on multi-user dialogues and generalizes to unseen domains. |
NewsDialogues: Towards Proactive News Grounded Conversation (2023.findings-acl)
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Siheng Li, Yichun Yin, Cheng Yang, Wangjie Jiang, Yiwei Li, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang
| Challenge: | Hot news is one of the most popular topics in daily conversations. |
| Approach: | They propose a task where a dialogue system can lead the conversation based on key topics of the news. |
| Outcome: | The proposed method can lead conversations based on key topics of the news . it can also be used in information-seeking and chit-chat scenarios . |
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)
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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji
| Challenge: | a framework for constructing dialogue world models for natural language tasks is currently lacking. |
| Approach: | They propose a framework that can be used to train a dialogue world model. |
| Outcome: | The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification . |
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System (2021.acl-long)
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| Challenge: | Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. |
| Approach: | They introduce a task, Novel Slot Detection, in the task-oriented dialogue system. |
| Outcome: | The proposed task is based on two public NSD datasets and proposes strong baselines . it aims to identify a sequence of tokens and extract semantic constituents from user queries . |
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles (2020.findings-emnlp)
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Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Milica Gasic
| Challenge: | Current models for dialogue state tracking only achieve 55% accuracy . however, they lack in performance compared to belief trackers and do not produce well calibrated distributions. |
| Approach: | They propose to calibrate a model for dialogue belief trackers to measure dialogue state accuracy. |
| Outcome: | The proposed model outperforms existing models in terms of accuracy and accuracy. |
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration (2021.findings-emnlp)
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| Challenge: | Persuasion dialogue systems have long-standing problems of dialogue repetition and inconsistency which could impact user experience and impede the persuaded outcome. |
| Approach: | They propose to refine a language model baseline without user simulators and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation results on a donation persuasion task and generates more diverse, consistent and persuasive conversations according to user feedback. |
PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling (2026.findings-eacl)
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| Challenge: | Existing approaches to mental health dialogue are reactive and lack systematic user state modeling for proactive therapeutic exploration. |
| Approach: | They propose a dialogue system designed for the exploration phase of counseling that systematically tracks user psychological states through the PPPPPI framework augmented with cognitive error detection. |
| Outcome: | The proposed system outperforms baseline and ablation modes in automatic evaluation and expert evaluation by a certified counselor. |
Generating Responses with a Specific Emotion in Dialog (P19-1)
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| Challenge: | EmoDS can express emotions in both ways, but it is difficult to scale to large datasets. |
| Approach: | They propose an emotional dialog system that can express emotions in both ways . they use strong emotional words and neutral words to increase the intensity of emotions . |
| Outcome: | The proposed system performs better than baselines in BLEU, diversity and quality of emotional expression. |
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations (2023.findings-emnlp)
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| Challenge: | Recent work on NLP explainability methods lacks a dialogue-based interpretability framework that can convey faithful explanations in human-understandable terms. |
| Approach: | They adapt the conversational explanation framework TalkToModel to the NLP domain and add new NLP-specific operations such as free-text rationalization to illustrate its generalizability. |
| Outcome: | The proposed framework can be used to explain models on three NLP tasks and is generalizable to different datasets, use cases and models. |
Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)
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| Challenge: | Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses. |
| Approach: | They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models. |
| Outcome: | The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset . |
A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection (2020.coling-main)
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| Challenge: | Existing models for dialogue breakdown detection do not focus on preventing dialogue breakdowns. |
| Approach: | They propose a model that integrates a pretrained cross-lingual language model and a co-attention network for dialogue breakdown detection. |
| Outcome: | The proposed model outperforms all previous approaches on evaluation metrics in Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 . |
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2023.acl-long)
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| Challenge: | MMDialog is a dataset of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics. |
| Approach: | They propose to use a curated set of 1.08 million dialogues with 1.53 million unique images to generalize the open domain. |
| Outcome: | The proposed system can predict responses to multi-modal content with state-of-the-art techniques and measure their performance. |
Analysis of Dialogue in Human-Human Collaboration in Minecraft (2022.lrec-1)
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| Challenge: | Recent studies have focused on developing dialogue systems that enable collaborative work, but few studies have centered on creative collaborative work. |
| Approach: | They collected 500 dialogues of human-human collaboration in Minecraft as a basis for developing a dialogue system that enables creative collaborative work. |
| Outcome: | The proposed system can be used to create a collaborative garden in Minecraft and collect text chats, action logs, and subjective evaluations. |
Analysis of Implicit Conditions in Database Search Dialogues (L18-1)
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| Challenge: | Annotators annotated 50 database search dialogues with database field tags . 10% of the utterances included non-database-field information, authors say . |
| Approach: | They propose to annotate database search dialogues on real estate and analyse their utterances for database queries. |
| Outcome: | The proposed method can extract the implicit conditions from user utterances and construct queries. |
Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation (2024.findings-acl)
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| Challenge: | Several datasets have been developed for building conversational question answering systems. |
| Approach: | They propose a robust dialog synthesising method that learns segmentation instead of using sentence boundaries. |
| Outcome: | The proposed method achieves superior quality when compared to WikiDialog . it also improves performance across OR-QuAC benchmarks . |
Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process (2023.acl-long)
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| Challenge: | Existing estimators measure performance by user satisfaction but ignore satisfaction dynamics across turns. |
| Approach: | They propose to use user satisfaction estimation to estimate performance of dialogue systems by using an estimator to simulate users. |
| Outcome: | The proposed estimator outperforms existing estimators on four benchmark dialogue datasets. |
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)
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| 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. |
Beyond Candidates : Adaptive Dialogue Agent Utilizing Persona and Knowledge (2023.findings-emnlp)
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| Challenge: | a previous study suggested that human dialogue systems ground persona and knowledge but they require incomplete candidate sets. |
| Approach: | They propose an adaptive dialogue agent that uses persona and knowledge without candidate sets . their model generates consistent and relevant persona descriptions and identifies relevant knowledge . |
| Outcome: | The proposed model outperforms baselines that ground persona and knowledge candidates even with fragmentary information. |
Deep Unknown Intent Detection with Margin Loss (P19-1)
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| Challenge: | Existing methods for detecting unknown intents are difficult due to lack of examples. |
| Approach: | They propose a method for detecting unknown intents using bidirectional long-term memory networks with the margin loss as the feature extractor. |
| Outcome: | The proposed method can yield consistent improvements on two benchmark datasets. |
From Traits to Empathy: Personality-Aware Multimodal Empathetic Response Generation (2025.coling-main)
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| Challenge: | Existing approaches focus on acquiring affective and cognitive knowledge from text, but neglect the unique personality traits of individuals and the inherently multimodal nature of human face-to-face conversation. |
| Approach: | They propose a multimodal dialogue system that generates empathetic responses from a perspective that considers the personality traits of users. |
| Outcome: | The proposed system generates empathetic responses from a multimodal perspective and analyzes multimodal data to understand the user’s emotional state and situation. |
Domain Adaptation for Conversational Query Production with the RAG Model Feedback (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on human-annotated search queries but they can not cover conversations of various domains. |
| Approach: | They propose a domain adaptation framework that uses retrieval-augmented generation to improve the model's robustness. |
| Outcome: | The proposed model is more robust and performs significantly better in a more challenging setting over strong baselines. |
Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task (2022.lrec-1)
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| Challenge: | Existing studies on the process of building common ground have not been well conducted. |
| Approach: | They propose a method for recording the process of building common ground through a dialogue by using the intermediate result of a task. |
| Outcome: | The proposed method can record the building common ground process by using the intermediate result of a task and can be estimated quite accurately. |
Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task (2022.lrec-1)
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| Challenge: | entrainment is a phenomenon in which interlocutors start speaking more similarly to each other. |
| Approach: | They propose to use crowd-sourced data to study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. |
| Outcome: | The proposed system adapts the user's descriptive strategy to one that is simpler to parse for the natural language understanding unit without impinging on their creativity. |
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance (2021.emnlp-main)
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Carel van Niekerk, Andrey Malinin, Christian Geishauser, Michael Heck, Hsien-chin Lin, Nurul Lubis, Shutong Feng, Milica Gasic
| Challenge: | Neural dialogue belief trackers that take uncertainty into account are often overconfident in their decisions and therefore less robust. |
| Approach: | They propose to use different uncertainty measures in neural belief tracking to integrate uncertainty into the feature space of the policy and train policies through interaction with a user simulator. |
| Outcome: | The proposed approach improves both performance and robustness of the downstream dialogue policy. |
Annotating Reflections for Health Behavior Change Therapy (L18-1)
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| Challenge: | Existing studies show that depression can be treated by Motivational Interviewing (MI) |
| Approach: | They annotated reflections, an essential counselor behavioral code in motivational interviewing for psychotherapy on conversations that are a combination of casual and therapeutic dialogue. |
| Outcome: | The annotated transcripts are a vital resource for automated health behavior change therapy . the corpus is being constructed and annotating conversations by one annotator . |
We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses (2023.emnlp-main)
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| Challenge: | a variety of personas can be elicited from large language models, but they are opaque and unpredictable. |
| Approach: | They propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses. |
| Outcome: | The proposed method captures habitual knowledge and generates persona-based responses from a large language model. |
Mixed-Session Conversation with Egocentric Memory (2024.findings-emnlp)
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| Challenge: | Recent dialogue systems exhibit an inability to replicate dynamic, continuous, long-term interactions involving multiple partners. |
| Approach: | They propose a multi-session dialogue system that builds on real-world interactions by integrating deep layered interactions and widening conversation networks. |
| Outcome: | The proposed system is based on a dataset of 6 consecutive dialogue episodes with four speakers (one main speaker and three partners) appearing in each episode. |
Disfluency Generation for More Robust Dialogue Systems (2023.findings-acl)
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| 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. |
User Willingness-aware Sales Talk Dataset (2025.coling-main)
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| Challenge: | Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. |
| Approach: | They developed a user willingness–aware sales talk collection by leveraging the ecological validity concept to elicit natural user willingness. |
| Outcome: | The proposed system elicited user willingness at the utterance level from multiple perspectives and was able to improve the user's intent to purchase. |
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning (2024.findings-acl)
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Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley
| Challenge: | Motivational Interviewing (MI) requires a system that can infer how to motivate users to adopt positive lifestyle changes. |
| Approach: | They propose a framework that can learn and apply conversation strategies from expert demonstrations by using natural language inductive rules. |
| Outcome: | The proposed framework outperforms in-context demonstrations that are over 50 times longer and can learn natural language strategies from demonstrations. |
I Remember You!: SUI Corpus for Remembering and Utilizing Users’ Information in Chat-oriented Dialogue Systems (2024.lrec-main)
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| Challenge: | Existing methods for remembering and utilizing information on users in system utterances do not always fit the context of the dialogue. |
| Approach: | They propose to use user information to fill in utterance templates but the utterrances do not always fit the context. |
| Outcome: | The proposed system can remember and utilize user information on users in dialogues while keeping appropriateness for the context. |
Don’t Forget Your ABC’s: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems (2023.acl-long)
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| Challenge: | Existing evaluation methods are biased because of their subjectivity and inconsistent evaluation can misinform the performance of a chat-oriented open-domain dialogue system. |
| Approach: | They propose to use a human evaluation method to estimate the rates of manypasted macro ‘LN’ dialogue system behaviors to compare them with existing evaluation methods. |
| Outcome: | The proposed method is more suitable than alternative Likert-style or comparative approaches for dimensional evaluation of open-domain dialogue systems. |
Would You Like to Make a Donation? A Dialogue System to Persuade You to Donate (2024.lrec-main)
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| Challenge: | Persuasive automated dialogue systems are a popular way to influence people's behavior and decision making. |
| Approach: | They propose to use a context-aware persuasion strategy selection module to persult users . they also propose a persuasiveness prediction model to automatically evaluate the persuasiveness of generated text. |
| Outcome: | The proposed system can achieve better performance on several automated evaluation metrics than baseline models. |