Suket Arora, Kamaljeet Batra, Sarabjit Singh
A Dialogue System is a system which interacts with human in natural language. At present many universities are developing the dialogue system in their regional language. This paper will discuss about dialogue system, its components, challenges and its evaluation. This paper helps the researchers for getting info regarding dialogues system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
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In contrast with goal-oriented dialogue, social dialogue has no clear measure of task success. Consequently, evaluation of these systems is notoriously hard. In this paper, we review current evaluation methods, focusing on automatic metrics. We conclude that turn-based metrics often ignore the context and do not account for the fact that several replies are valid, while end-of-dialogue rewards are mainly hand-crafted. Both lack grounding in human perceptions.
Building dialogue systems that naturally converse with humans is being an attractive and an active research domain. Multiple systems are being designed everyday and several datasets are being available. For this reason, it is being hard to keep an up-to-date state-of-the-art. In this work, we present the latest and most relevant retrieval-based dialogue systems and the available datasets used to build and evaluate them. We discuss their limitations and provide insights and guidelines for future work.
Evaluating the quality of a dialogue system is an understudied problem. The recent evolution of evaluation method motivated this survey, in which an explicit and comprehensive analysis of the existing methods is sought. We are first to divide the evaluation methods into three classes, i.e., automatic evaluation, human-involved evaluation and user simulator based evaluation. Then, each class is covered with main features and the related evaluation metrics. The existence of benchmarks, suitable for the evaluation of dialogue techniques are also discussed in detail. Finally, some open issues are …
This paper describes a recently initiated research project aiming at supporting development of computerised dialogue systems that handle breaches of conversational norms such as the Gricean maxims, which describe how dialogue participants ideally form their utterances in order to be informative, relevant, brief, etc. Our approach is to model dialogue and norms with co-operating distributed grammar systems (CDGSs), and to develop methods to detect breaches and to handle them in dialogue systems for verbal human-robot interaction.
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements. Although there have been many explorations in negotiation dialogue systems, a systematic review of this task has to date remained notably absent. To this end, we aim to fill this gap by reviewing contemporary studies in the emerging field of negotiation dialogue systems, covering benchmarks, eva…
In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. Dialogue systems are increasingly being designed to move beyond just imitating conversation and also improve from such interactions over time. In this survey, we present a broad overview of methods developed to build dialogue systems over the years. Different use cases for dialogue systems ranging from task-based systems to open domain chatbots motivate and necessitate specific systems. Starting from simple rule-based systems, research has progressed towards in…
Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations
本文系统综述122项任务导向对话系统评估研究，梳理构念、指标与操作化问题，并结合大型语言模型提出未来研究议程和评估建议。
Task-oriented dialogue systems (TODS) are continuing to rise in popularity as various industries find ways to effectively harness their capabilities, saving both time and money. However, even state-of-the-art TODS are not yet reaching their full potential. TODS typically have a primary design focus on completing the task at hand, so the metric of task-resolution should take priority. Other conversational quality attributes that may point to the success, or otherwise, of the dialogue, may be ignored. This can cause interactions between human and dialogue system that leave the user dissatisfied …
Talking with Machines: A Comprehensive Survey of Emergent Dialogue Systems
本文全面综述对话系统的历史发展、核心方法、训练数据集及评估指标，涵盖传统系统与基于Transformer的先进方法，并展望未来挑战与前景。
While communicating with a user, a task-oriented dialogue system has to track the user's needs at each turn according to the conversation history. This process called dialogue state tracking (DST) is crucial because it directly informs the downstream dialogue policy. DST has received a lot of interest in recent years with the text-to-text paradigm emerging as the favored approach. In this review paper, we first present the task and its associated datasets. Then, considering a large number of recent publications, we identify highlights and advances of research in 2021-2022. Although neural appr…
Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present \textbf{DiSCoL} (\textbf{Di}alogue \textbf{S}ystems through \textbf{Co}versational \textbf{L}ine guided response generation). DiSCoL is an open-domain dialog…
Let's Negotiate! A Survey of Negotiation Dialogue Systems
该综述针对协商对话系统领域缺乏系统性梳理的空白，调研了该领域近期研究的基准、评估与方法，探讨了多模态等未来方向，为学界提供系统概览并启发后续研究。
Unsupervised Flow Discovery from Task-oriented Dialogues
本文提出一种从任务导向对话历史中无监督发现对话流程的方法，通过话语向量聚类构建状态转移图，并在MultiWOZ上验证了其提取有意义流程的潜力。
A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
本文综述了基于大型语言模型的多轮对话系统，总结了LLMs及适配方法，阐述了开放域与任务型对话系统的最新进展、数据集与评估指标，并探讨了未来研究方向。
General natural dialogue processing requires large amounts of domain knowledge as well as linguistic knowledge in order to ensure acceptable coverage and understanding. There are several ways of integrating lexical resources (e.g. dictionaries, thesauri) and knowledge bases or ontologies at different levels of dialogue processing. We concentrate in this paper on how to exploit domain knowledge for filtering interpretation hypotheses generated by a robust semantic parser. We use domain knowledge to semantically constrain the hypothesis space. Moreover, adding an inference mechanism allows us to…
Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to the outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of mode…
Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet. Automatically summarizing such task-oriented dialogues can help a business collect and review needs to improve the service. Besides, previous datasets pay more attention to generate good summaries with higher ROUGE scores, but they hardly understand the structured information of dialogues and ignore the factuality of summaries. In this paper, we introduce a large-scale public Task-Oriented Dialogue Summariza…
Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training objectives like negative log-likelihood (NLL) or cross-entropy. Such commonly used training objectives do not foster generating alternate responses to a context. But, the effects of minimizing an alternate training objective that fosters a model to generate alternate response and score it on semantic similarity has not been well studied. We hypothesize that a language generation model can improve on its diversity by learning to generate alternate text durin…
Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue
本文提出通过生成合成数据和训练专用对话代理，提升元评审效率。
Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and g…
Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations
本文系统综述了个性化对话生成的近期研究，涵盖22个数据集、2021-2023年间17项顶级会议论文及LLMs进展，总结了方法论、评估指标与未来挑战。
Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions
本文将同行评审重构为作者、审稿人和决策者参与的多轮长上下文对话，构建了包含26,841篇论文和92,017条评审的数据集，并提出多角色评估指标，为LLM驱动的动态同行评审提供了新基础。
Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey
该文献综述梳理了协作对话系统中对话管理从传统方法到神经时代的演进，分析了数据驱动及神经方法的应用与主流趋势，为人机协作对话管理的未来发展及大语言模型潜力挖掘提供基础背景。
Many socio-linguistic cues are used in the conversational analysis, such as emotion, sentiment, and dialogue acts. One of the fundamental social cues is politeness, which linguistically possesses properties useful in conversational analysis. This short article presents some of the brief findings of polite emotional dialogue acts, where we can correlate the relational bonds between these socio-linguistics cues. We found that the utterances with emotion classes Anger and Disgust are more likely to be impolite while Happiness and Sadness to be polite. Similar phenomenon occurs with dialogue acts,…
Dialogue summarization aims to condense the original dialogue into a shorter version covering salient information, which is a crucial way to reduce dialogue data overload. Recently, the promising achievements in both dialogue systems and natural language generation techniques drastically lead this task to a new landscape, which results in significant research attentions. However, there still remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field carefully and widely. In detail, we systematically organize …
CADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization
本文系统综述2019-2024年间1262篇论文，梳理抽象式对话摘要的语言、结构、理解等六类挑战及其技术关联，指出理解、事实性等难题仍待攻克，并探讨了大语言模型的影响。
The rapid development and application of natural language generation (NLG) techniques has revolutionized the field of automatic text production. However, these techniques are still limited in their ability to produce human-like text that is truly reasonable and informative. In this paper, we explore the importance of NLG being guided by knowledge, in order to convey human-like reasoning through language generation. We propose ten goals for intelligent NLG systems to pursue, and briefly review the achievement of NLG techniques guided by knowledge and reasoning. We also conclude by envisioning f…
Domain Adaptation in Intent Classification Systems: A Review
本文从技术角度对对话系统中的意图分类进行了系统综述，全面梳理了所需的数据集、领域、任务和方法，分析了意图分类的难点及领域自适应的局限，并为未来研究提供了方向。
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
本文综述了强化学习在自然语言处理中的关键技术进展，并重点分析了其在医疗决策、对话系统、翻译和问答等医疗保健场景中的应用与伦理挑战。
In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. Our major finding is that after standard finetuning, the model forgets some of the important language generation skills acquired during large-scale pretraining. We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection. As a …
ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models
本文提出Review-Instruct框架，通过候选者、多个评审者和主席的迭代“提问-回答-评审”过程生成高质量多轮对话数据，微调LLaMA2-13B后在MMLU-Pro和MT-Bench上分别提升2.9%和2%，验证了评审驱动多智能体框架的有效性。
Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been designed with the rapid development of deep learning techniques, especially the recent pre-trained language models (PrLMs). Among these studies, the fundamental yet challenging type of task is dialogue comprehension whose role is to teach the machines to read and comprehend the dialogue context before responding. In this paper, we review the previous methods from the technical perspective of dialogue modeling for the dia…
Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been designed with the rapid development of deep learning techniques, especially the recent pre-trained language models (PrLMs). Among these studies, the fundamental yet challenging type of task is dialogue comprehension whose role is to teach the machines to read and comprehend the dialogue context before responding. In this paper, we review the previous methods from the technical perspective of dialogue modeling for the dia…
Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models
本文回顾了单词级或更低级别的增量交互系统文献，指出现有大型语言模型因处理单调性而缺乏增量修正能力，重点调研了对话管理器的增量建模，并为大型语言模型时代的具身机器人平台提出了实用增量对话管理的要求。
A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training
为解决医学培训中标准化病人稀缺问题，构建法语OSCE对话数据集，并提出基于LLM的可控虚拟患者生成管道，通过检索增强和反思循环提升真实性与一致性，实验表明可控性模块改善患者保真度和学生评估一致性。
Toward Real-World Chinese Psychological Support Dialogues: CPsDD Dataset and a Co-Evolving Multi-Agent System
针对中文心理支持数据集稀缺问题，提出微调大模型生成数据并构建CPsDD数据集，同时开发协同进化多智能体系统CADSS，在策略预测和ESC任务上达到最优性能。
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