与自然语言中用户互动的对话推荐系统（CRS）利用了在配对人类的帮助下收集的建议对话框，其中一个人扮演寻求者的角色，而另一个则是推荐人。这些建议对话包括项目和实体，以披露寻求者自然语言的偏好。但是，为了精确地对寻求者的偏好进行建模并始终如一地做出反应，主要是CRS依赖于对话框中出现的明确注释的项目和实体，通常会利用域知识。在这项工作中，我们调查了受启发的数据集，该数据集包含有关社交对话建议的建议对话框，其中使用自动关键字或模式匹配技术明确注释项目和实体。为此，我们发现了大量案例，这些案例和实体根本被错误注释或缺少注释。然而，这个问题仍然在何种程度上有效的注释有效。此外，目前尚不清楚穷人和改善注释对CRS总体有效性的相对影响在响应的一致性和质量方面是什么。在这方面，首先，我们手动修复了注释并删除了受启发数据集中的噪声。其次，我们使用两个版本的数据集评估了几个基准CR的性能。我们的分析表明，使用数据集的改进版本，即Inspired2，各种基准CRS的表现优于且对话框与使用原始版本的使用相比，具有丰富的知识概念。我们在https://github.com/ahtsham58/inspired2公开发布改进的数据集（Inspired2）。

会话推荐系统（CRS）是交互式代理，通过多转交谈来支持其用户与建议相关的目标。通常，可以在各个维度上评估CRS。今天的CRS主要依靠离线（计算）措施来评估其算法与不同基线相比的性能。但是，离线措施可能会有局限性，例如，当将新生成的响应与地面真理进行比较的指标与人类的看法无关时，因为在给定的对话情况下，各种替代性产生的响应可能也是合适的。因此，对基于机器学习的CRS模型的当前研究认识到人类在评估过程中的重要性，知道纯离线测量可能不足以评估CRS等高度交互式系统。

以任务为导向的对话系统（TDSS）主要在离线设置或人类评估中评估。评估通常仅限于单转或非常耗时。作为替代方案，模拟用户行为的用户模拟器使我们能够考虑一组广泛的用户目标，以生成类似人类的对话以进行模拟评估。使用现有的用户模拟器来评估TDSS是具有挑战性的，因为用户模拟器主要旨在优化TDSS的对话策略，并且评估功能有限。此外，对用户模拟器的评估是一个开放的挑战。在这项工作中，我们提出了一个用于端到端TDS评估的隐喻用户模拟器，如果它在与系统的交互中模拟用户的类似思维，则定义模拟器是隐喻的。我们还提出了一个基于测试人员的评估框架，以生成变体，即具有不同功能的对话系统。我们的用户模拟器构建了一个隐喻的用户模型，该模型通过参考遇到新项目时的先验知识来帮助模拟器进行推理。我们通过检查模拟器与变体之间的模拟相互作用来估计模拟器的质量。我们的实验是使用三个TDS数据集进行的。与基于议程的模拟器和三个数据集上的SEQ2SEQ模型相比，隐喻用户模拟器与手动评估的一致性更好。我们的测试人员框架展示了效率，并且可以更好地概括和可扩展性，因为它可以适用于多个域中的对话和多个任务，例如对话建议和电子商务对话。

会话推荐系统（CRS）已成为一个新兴的研究主题，试图通过交互式对话进行建议，这些对话通常由发电和建议模块组成。 CRS的先前工作倾向于将更多的外部和领域特定知识纳入项目评论，以提高性能。尽管事实的收集和注释特定于外部领域的信息需要大量的人类努力并脱离了普遍性，但过多的额外知识在它们之间带来了更大的困难。因此，我们建议从上下文中充分发现和提取内部知识。我们将实体级别和上下文级别的表示形式捕获为对建议的共同模拟用户的偏好，在这种情况下，时间吸引的注意力旨在强调实体级表示中最近出现的项目。我们进一步使用预训练的巴特来初始化生成模块，以减轻数据稀缺性并增强上下文建模。除了在流行数据集（REDIAIL）上进行实验外，我们还包括一个多域数据集（OpenDialKg）来显示我们模型的有效性。两个数据集的实验都表明，我们的模型在大多数评估指标上都具有更好的性能，其外部知识较少，并且可以很好地推广到其他领域。对建议和生成任务的其他分析证明了我们在不同情况下模型的有效性。

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.

Using chatbots to deliver recommendations is increasingly popular. The design of recommendation chatbots has primarily been taking an information-centric approach by focusing on the recommended content per se. Limited attention is on how social connection and relational strategies, such as self-disclosure from a chatbot, may influence users' perception and acceptance of the recommendation. In this work, we designed, implemented, and evaluated a social chatbot capable of performing three different levels of self-disclosure: factual information (low), cognitive opinions (medium), and emotions (high). In the evaluation, we recruited 372 participants to converse with the chatbot on two topics: movies and COVID-19 experiences. In each topic, the chatbot performed small talks and made recommendations relevant to the topic. Participants were randomly assigned to four experimental conditions where the chatbot used factual, cognitive, emotional, and adaptive strategies to perform self-disclosures. By training a text classifier to identify users' level of self-disclosure in real-time, the adaptive chatbot can dynamically match its self-disclosure to the level of disclosure exhibited by the users. Our results show that users reciprocate with higher-level self-disclosure when a recommendation chatbot consistently displays emotions throughout the conversation. Chatbot's emotional disclosure also led to increased interactional enjoyment and more positive interpersonal perception towards the bot, fostering a stronger human-chatbot relationship and thus leading to increased recommendation effectiveness, including a higher tendency to accept the recommendation. We discuss the understandings obtained and implications to future design.

Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features.

One of the biggest challenges of natural language generation (NLG) is the proper handling of named entities. Named entities are a common source of grammar mistakes such as wrong prepositions, wrong article handling, or incorrect entity inflection. Without factoring linguistic representation, such errors are often underrepresented when evaluating on a small set of arbitrarily picked argument values, or when translating a dataset from a linguistically simpler language, like English, to a linguistically complex language, like Russian. However, for some applications, broadly precise grammatical correctness is critical -- native speakers may find entity-related grammar errors silly, jarring, or even offensive. To enable the creation of more linguistically diverse NLG datasets, we release a Corpus of Linguistically Significant Entities (CLSE) annotated by linguist experts. The corpus includes 34 languages and covers 74 different semantic types to support various applications from airline ticketing to video games. To demonstrate one possible use of CLSE, we produce an augmented version of the Schema-Guided Dialog Dataset, SGD-CLSE. Using the CLSE's entities and a small number of human translations, we create a linguistically representative NLG evaluation benchmark in three languages: French (high-resource), Marathi (low-resource), and Russian (highly inflected language). We establish quality baselines for neural, template-based, and hybrid NLG systems and discuss the strengths and weaknesses of each approach.

会话推荐系统（CRS）旨在通过自然语言对话推荐给用户的合适项目。对于开发有效的CRSS，主​​要技术问题是如何准确地推断用户偏好从非常有限的对话环境。为了解决问题，有希望的解决方案是纳入外部数据以丰富上下文信息。然而，先前的研究主要集中在针对某些特定类型的外部数据量身定制的融合模型，这是不普遍的模型，并利用多型外部数据。为了有效利用多型外部数据，我们提出了一种新型粗对对比学习框架，以改善CRS的数据语义融合。在我们的方法中，我们首先从不同的数据信号中提取并代表多粒度语义单元，然后以粗略的方式对齐相关的多型语义单元。为了实现这一框架，我们设计了用于建模用户偏好的粗粒细粒和细粒度的程序，前者侧重于更通用，粗粒粗粒语义融合，后者侧重于更具体，细粒度的语义融合。可以扩展这样的方法以包含更多种类的外部数据。两个公共CRS数据集的大量实验已经证明了我们在两种建议和对话任务中的方法的有效性。

会话推荐系统（CRS）通过推断用户首选项从对话历史推断用户偏好，提供准确的建议，并生成适当的响应。以前的CRSS使用基于知识图（kg）的推荐模块，并将kg与语言模型集成为响应生成。虽然基于KG的方法证明有效，但仍有两个问题仍有待解决。首先，基于KG的方法忽略会话环境中的信息，但仅依赖于实体关系和单词包来推荐项目。其次，它需要实质性的工程努力来维持模型特定的关系的KG，从而导致灵活性更少。在本文中，我们提出了一种简单而有效的架构，包括预先接受了训练的语言模型（PLM）和项目元数据编码器。编码器学会将项目元数据映射到嵌入式，该嵌入式可以反映对话框上下文中的语义信息。然后，PLM将语义对齐的项目嵌入式与对话上下文一起消耗，以生成高质量的建议和响应。我们的模型通过直接将每个项目转换为嵌入来降低工程复杂性而不是建模实体关系。基准数据集重拨的实验结果表明，我们的模型在两种推荐和响应生成任务上获得最先进的结果。

This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. We further categorized the framework to ConvSearch and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems.

对话研究的最终目标是开发可以在交互式设置中有效使用的系统。为此，我们在第9对话系统技术挑战中介绍了对话框的交互式评估。该曲目由两个子任务组成。第一个子任务涉及建立知识接地的响应生成模型。第二个子任务旨在通过与真实用户的交互式设置进行评估，旨在将对话模型扩展到静态数据集之外。我们的曲目挑战参与者开发强大的响应生成模型，并探索将它们扩展到与真实用户的来回互动的策略。从静态语料库到交互式评估的发展引入了独特的挑战，并促进了对开放域对话系统的更全面评估。本文概述了曲目，包括方法和结果。此外，它提供了有关如何最佳评估开放域对话框模型的见解

在语言处理的神经方法上的最新进展引发了人们对建立智能开放域聊天机器人的兴趣的复兴。但是，即使是最先进的神经聊天机器人也无法在对话框中每个回合产生令人满意的响应。一个实用的解决方案是为相同上下文生成多个响应候选者，然后执行响应排名/选择以确定哪个候选者是最好的。先前的响应选择中的工作通常使用从现有对话框形成的合成数据来训练响应排名者，通过使用地面真理响应作为单个适当的响应并通过随机选择或使用对抗方法来构建不适当的响应。在这项工作中，我们策划了一个数据集，其中为适当的（正）和不适当（负）手动注释了为相同对话框上下文产生的多个响应发生器的响应。我们认为，这样的培训数据可以更好地匹配实际的用例示例，从而使模型能够有效地对响应进行排名。有了这个新数据集，我们对最先进的响应选择方法进行了系统的评估，并证明，使用多个积极候选者和使用手动验证的硬性负面候选者的两种策略都可以与使用相比，可以带来重大的绩效提高对抗性训练数据，例如，召回@1分别增加了3％和13％。

Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress towards models that do not exhibit this issue requires evaluation metrics that can quantify its prevalence. To this end, we introduce the Benchmark for Evaluation of Grounded INteraction (BEGIN), comprised of 12k dialogue turns generated by neural dialogue systems trained on three knowledgegrounded dialogue corpora. We collect human annotations assessing the extent to which the models' responses can be attributed to the given background information. We then use BEGIN to analyze eight evaluation metrics. We find that these metrics rely on spurious correlations, do not reliably distinguish attributable abstractive responses from unattributable ones, and perform substantially worse when the knowledge source is longer. Our findings underscore the need for more sophisticated and robust evaluation metrics for knowledge-grounded dialogue. We make BEGIN publicly available at https://github.com/ google/BEGIN-dataset.

Conversational recommender systems (CRS) aim to employ natural language conversations to suggest suitable products to users. Understanding user preferences for prospective items and learning efficient item representations are crucial for CRS. Despite various attempts, earlier studies mostly learned item representations based on individual conversations, ignoring item popularity embodied among all others. Besides, they still need support in efficiently capturing user preferences since the information reflected in a single conversation is limited. Inspired by collaborative filtering, we propose a collaborative augmentation (COLA) method to simultaneously improve both item representation learning and user preference modeling to address these issues. We construct an interactive user-item graph from all conversations, which augments item representations with user-aware information, i.e., item popularity. To improve user preference modeling, we retrieve similar conversations from the training corpus, where the involved items and attributes that reflect the user's potential interests are used to augment the user representation through gate control. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our method. Our code and data are available at https://github.com/DongdingLin/COLA.

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems 1 .

这项工作提出了一个新的对话数据集，即cookdial，该数据集促进了对任务知识了解的面向任务的对话系统的研究。该语料库包含260个以人类对任务为导向的对话框，其中代理给出了配方文档，指导用户烹饪菜肴。 Cookdial中的对话框展示了两个独特的功能：（i）对话流与支持文档之间的程序对齐； （ii）复杂的代理决策涉及分割长句子，解释硬说明并在对话框上下文中解决核心。此外，我们在假定的面向任务的对话框系统中确定了三个具有挑战性的（子）任务：（1）用户问题理解，（2）代理操作框架预测和（3）代理响应生成。对于这些任务中的每一个，我们都会开发一个神经基线模型，我们在cookdial数据集上进行了评估。我们公开发布烹饪数据集，包括对话框和食谱文档的丰富注释，以刺激对特定于域的文档接地对话框系统的进一步研究。

会话推荐系统（CRS）旨在捕获用户的当前意图，并通过实时多转交流交互提供建议。作为人机互动系统，CRS必须改善用户体验。但是，大多数CRS方法忽略了用户体验的重要性。在本文中，我们为CRS提出了两个关键点，以改善用户体验：（1）像人类一样说话，人类可以根据当前的对话环境以不同的风格说话。 （2）识别精细颗粒的意图，即使对于相同的话语，不同的用户也具有多种良好的意图，这与用户的固有偏好有关。根据观察结果，我们提出了一个新颖的CRS模型，即创建的定制对话推荐系统（CCRS），该系统从三个角度从三个角度定制了用户的CRS模型。对于类似人类的对话服务，我们提出了多式对话响应生成器，该响应响应生成器选择了语音发言的上下文感知语言风格。为了提供个性化的建议，我们在用户固有的偏好的指导下从对话上下文中提取用户当前的细粒度意图。最后，为了自定义每个用户的模型参数，我们从元学习的角度训练模型。广泛的实验和一系列分析表明，我们的CCR在推荐和对话服务上的优势。

问答系统被认为是流行且经常有效的信息在网络上寻求信息的手段。在这样的系统中，寻求信息者可以通过自然语言提出问题来获得对他们的查询的简短回应。交互式问题回答是一种最近提出且日益流行的解决方案，它位于问答和对话系统的交集。一方面，用户可以以普通语言提出问题，并找到对她的询问的实际回答；另一方面，如果在初始请求中有多个可能的答复，很少或歧义，则系统可以将问题交通会话延长到对话中。通过允许用户提出更多问题，交互式问题回答使用户能够与系统动态互动并获得更精确的结果。这项调查提供了有关当前文献中普遍存在的交互式提问方法的详细概述。它首先要解释提问系统的基本原理，从而定义新的符号和分类法，以将所有已确定的作品结合在统一框架内。然后，根据提出的方法，评估方法和数据集/应用程序域来介绍和检查有关交互式问题解答系统的审查已发表的工作。我们还描述了围绕社区提出的特定任务和问题的趋势，从而阐明了学者的未来利益。 GitHub页面的综合综合了本文献研究中涵盖的所有主要主题，我们的工作得到了进一步的支持。 https://sisinflab.github.io/interactive-question-answering-systems-survey/

Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.