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.
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In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.
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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/
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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.
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社交媒体网络已成为人们生活的重要方面,它是其思想,观点和情感的平台。因此,自动化情绪分析(SA)对于以其他信息来源无法识别人们的感受至关重要。对这些感觉的分析揭示了各种应用,包括品牌评估,YouTube电影评论和医疗保健应用。随着社交媒体的不断发展,人们以不同形式发布大量信息,包括文本,照片,音频和视频。因此,传统的SA算法已变得有限,因为它们不考虑其他方式的表现力。通过包括来自各种物质来源的此类特征,这些多模式数据流提供了新的机会,以优化基于文本的SA之外的预期结果。我们的研究重点是多模式SA的最前沿领域,该领域研究了社交媒体网络上发布的视觉和文本数据。许多人更有可能利用这些信息在这些平台上表达自己。为了作为这个快速增长的领域的学者资源,我们介绍了文本和视觉SA的全面概述,包括数据预处理,功能提取技术,情感基准数据集以及适合每个字段的多重分类方法的疗效。我们还简要介绍了最常用的数据融合策略,并提供了有关Visual Textual SA的现有研究的摘要。最后,我们重点介绍了最重大的挑战,并调查了一些重要的情感应用程序。
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本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片(Sota)。它填补了空白,因为现有的调查文章在其范围内或被约会。我们包括两个重要方面,目前正在挖掘和建模社交媒体的重要性:动态和网络。社会动态对于了解影响影响或疾病的传播,友谊的形成,友谊的形成等,另一方面,可以捕获各种复杂关系,提供额外的洞察力和识别否则将不会被注意的重要模式。
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在这个大数据时代,当前一代很难从在线平台中包含的大量数据中找到正确的数据。在这种情况下,需要一个信息过滤系统,可以帮助他们找到所需的信息。近年来,出现了一个称为推荐系统的研究领域。推荐人变得重要,因为他们拥有许多现实生活应用。本文回顾了推荐系统在电子商务,电子商务,电子资源,电子政务,电子学习和电子生活中的不同技术和发展。通过分析有关该主题的最新工作,我们将能够详细概述当前的发展,并确定建议系统中的现有困难。最终结果为从业者和研究人员提供了对建议系统及其应用的必要指导和见解。
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通过整合人类的知识和经验,人在循环旨在以最低成本培训准确的预测模型。人类可以为机器学习应用提供培训数据,并直接完成在基于机器的方法中对管道中计算机中的难以实现的任务。在本文中,我们从数据的角度调查了人类循环的现有工作,并将它们分为三类具有渐进关系:(1)从数据处理中提高模型性能的工作,(2)通过介入模型培训提高模型性能,(3)系统的设计独立于循环的设计。使用上述分类,我们总结了该领域的主要方法;随着他们的技术优势/弱点以及自然语言处理,计算机愿景等的简单分类和讨论。此外,我们提供了一些开放的挑战和机遇。本调查打算为人类循环提供高级别的摘要,并激励有兴趣的读者,以考虑设计有效的循环解决方案的方法。
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Artificial intelligence and natural language processing (NLP) are increasingly being used in customer service to interact with users and answer their questions. The goal of this systematic review is to examine existing research on the use of NLP technology in customer service, including the research domain, applications, datasets used, and evaluation methods. The review also looks at the future direction of the field and any significant limitations. The review covers the time period from 2015 to 2022 and includes papers from five major scientific databases. Chatbots and question-answering systems were found to be used in 10 main fields, with the most common use in general, social networking, and e-commerce areas. Twitter was the second most commonly used dataset, with most research also using their own original datasets. Accuracy, precision, recall, and F1 were the most common evaluation methods. Future work aims to improve the performance and understanding of user behavior and emotions, and address limitations such as the volume, diversity, and quality of datasets. This review includes research on different spoken languages and models and techniques.
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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.
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人工智能(AI)使机器能够从人类经验中学习,适应新的输入,并执行人类的人类任务。 AI正在迅速发展,从过程自动化到认知增强任务和智能流程/数据分析的方式转换业务方式。然而,人类用户的主要挑战是理解和适当地信任AI算法和方法的结果。在本文中,为了解决这一挑战,我们研究并分析了最近在解释的人工智能(XAI)方法和工具中所做的最新工作。我们介绍了一种新颖的XAI进程,便于生产可解释的模型,同时保持高水平的学习性能。我们提出了一种基于互动的证据方法,以帮助人类用户理解和信任启用AI的算法创建的结果和输出。我们在银行域中采用典型方案进行分析客户交易。我们开发数字仪表板以促进与算法的互动结果,并讨论如何提出的XAI方法如何显着提高数据科学家对理解启用AI的算法结果的置信度。
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深度学习属于人工智能领域,机器执行通常需要某种人类智能的任务。类似于大脑的基本结构,深度学习算法包括一种人工神经网络,其类似于生物脑结构。利用他们的感官模仿人类的学习过程,深入学习网络被送入(感官)数据,如文本,图像,视频或声音。这些网络在不同的任务中优于最先进的方法,因此,整个领域在过去几年中看到了指数增长。这种增长在过去几年中每年超过10,000多种出版物。例如,只有在医疗领域中的所有出版物中覆盖的搜索引擎只能在Q3 2020中覆盖所有出版物的子集,用于搜索术语“深度学习”,其中大约90%来自过去三年。因此,对深度学习领域的完全概述已经不可能在不久的将来获得,并且在不久的将来可能会难以获得难以获得子场的概要。但是,有几个关于深度学习的综述文章,这些文章专注于特定的科学领域或应用程序,例如计算机愿景的深度学习进步或在物体检测等特定任务中进行。随着这些调查作为基础,这一贡献的目的是提供对不同科学学科的深度学习的第一个高级,分类的元调查。根据底层数据来源(图像,语言,医疗,混合)选择了类别(计算机愿景,语言处理,医疗信息和其他工程)。此外,我们还审查了每个子类别的常见架构,方法,专业,利弊,评估,挑战和未来方向。
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The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.
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We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda capturing complex computational legal processes, and embedding them in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.
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在科学研究中,该方法是解决科学问题和关键研究对象的必不可少手段。随着科学的发展,正在提出,修改和使用许多科学方法。作者在抽象和身体文本中描述了该方法的详细信息,并且反映该方法名称的学术文献中的关键实体称为方法实体。在大量的学术文献中探索各种方法实体有助于学者了解现有方法,为研究任务选择适当的方法并提出新方法。此外,方法实体的演变可以揭示纪律的发展并促进知识发现。因此,本文对方法论和经验作品进行了系统的综述,重点是从全文学术文献中提取方法实体,并努力使用这些提取的方法实体来建立知识服务。首先提出了本综述涉及的关键概念的定义。基于这些定义,我们系统地审查了提取和评估方法实体的方法和指标,重点是每种方法的利弊。我们还调查了如何使用提取的方法实体来构建新应用程序。最后,讨论了现有作品的限制以及潜在的下一步。
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各种网络的部署(例如,事物互联网(IOT)和移动网络),数据库(例如,营养表和食品组成数据库)和社交媒体(例如,Instagram和Twitter)产生大量的多型食品数据,这在食品科学和工业中起着关键作用。然而,由于众所周知的数据协调问题,这些多源食品数据显示为信息孤岛,导致难以充分利用这些食物数据。食物知识图表提供了统一和标准化的概念术语及其结构形式的关系,因此可以将食物信息孤单转换为更可重复使用的全球数量数字连接的食物互联网以使各种应用有益。据我们所知,这是食品科学与工业中食品知识图表的第一个全面审查。我们首先提供知识图表的简要介绍,然后主要从食物分类,食品本体到食品知识图表的进展。粮食知识图表的代表性应用将在新的配方开发,食品可追溯性,食物数据可视化,个性化饮食推荐,食品搜索和质询回答,视觉食品对象识别,食品机械智能制造方面来概述。我们还讨论了该领域的未来方向,例如食品供应链系统和人类健康的食品知识图,这应该得到进一步的研究。他们的巨大潜力将吸引更多的研究努力,将食物知识图形应用于食品科学和工业领域。
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过去十年已经看到人工智能(AI)的显着进展,这导致了用于解决各种问题的算法。然而,通过增加模型复杂性并采用缺乏透明度的黑匣子AI模型来满足这种成功。为了响应这种需求,已经提出了说明的AI(Xai)以使AI更透明,从而提高关键结构域中的AI。虽然有几个关于Xai主题的Xai主题的评论,但在Xai中发现了挑战和潜在的研究方向,这些挑战和研究方向被分散。因此,本研究为Xai组织的挑战和未来的研究方向提出了系统的挑战和未来研究方向:(1)基于机器学习生命周期的Xai挑战和研究方向,基于机器的挑战和研究方向阶段:设计,开发和部署。我们认为,我们的META调查通过为XAI地区的未来探索指导提供了XAI文学。
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Chatbots已经彻底改变了人类与计算机系统互动的方式,他们替代了服务代理,呼叫中心代表等。健身行业一直是一种不断增长的行业,尽管它还没有适应AI,ML和云计算等最新技术。在本文中,我们建议使用IBM Watson开发适合健身管理的聊天栏,并将其与Web应用程序集成。我们建议使用自然语言处理(NLP)和自然语言理解(NLU)以及为Chatbot Assistant提供的IBM Cloud Watson框架。该软件采用无服务器架构,通过提供饮食计划,家庭练习,互动咨询会,健身建议,将专业的服务结合起来。
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最近的自主代理和机器人的应用,如自动驾驶汽车,情景的培训师,勘探机器人和服务机器人带来了关注与当前生成人工智能(AI)系统相关的至关重要的信任相关挑战。尽管取得了巨大的成功,基于连接主义深度学习神经网络方法的神经网络方法缺乏解释他们对他人的决策和行动的能力。没有符号解释能力,它们是黑色盒子,这使得他们的决定或行动不透明,这使得难以信任它们在安全关键的应用中。最近对AI系统解释性的立场目睹了可解释的人工智能(XAI)的几种方法;然而,大多数研究都专注于应用于计算科学中的数据驱动的XAI系统。解决越来越普遍的目标驱动器和机器人的研究仍然缺失。本文评论了可解释的目标驱动智能代理和机器人的方法,重点是解释和沟通代理人感知功能的技术(示例,感官和愿景)和认知推理(例如,信仰,欲望,意图,计划和目标)循环中的人类。审查强调了强调透明度,可辨与和持续学习以获得解释性的关键策略。最后,本文提出了解释性的要求,并提出了用于实现有效目标驱动可解释的代理和机器人的路线图。
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Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how HCI and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 85 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, fairness, usability, and human-AI team performance. Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.
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