Topic
Chatbot
About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.
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TL;DR: Users’ idealism orientation was a significant factor in explaining use of such offensive language, and users’ perceived human-likeness of chatbot increased their likelihood of using offensive words targeting dislikable acquaintances, racial/ethnic groups, and political parties.
22 citations
01 Nov 2020
TL;DR: This paper introduces Item Response Theory for chatbot evaluation, using a paired comparison in which annotators judge which system responds better to the next turn of a conversation.
Abstract: Conversational agent quality is currently assessed using human evaluation, and often requires an exorbitant number of comparisons to achieve statistical significance. In this paper, we introduce Item Response Theory (IRT) for chatbot evaluation, using a paired comparison in which annotators judge which system responds better to the next turn of a conversation. IRT is widely used in educational testing for simultaneously assessing the ability of test takers and the quality of test questions. It is similarly well suited for chatbot evaluation since it allows the assessment of both models and the prompts used to evaluate them. We use IRT to efficiently assess chatbots, and show that different examples from the evaluation set are better suited for comparing high-quality (nearer to human performance) than low-quality systems. Finally, we use IRT to reduce the number of evaluation examples assessed by human annotators while retaining discriminative power.
22 citations
19 Nov 2019
TL;DR: The in-depth interviews with older adults and younger adults revealed that both groups were aligned in their prime motivation: They used chatbots to get their (simple) customer queries answered in a fast and convenient manner, however, they seemed to differ in their need for additional human contact.
Abstract: This qualitative interview study explores age differences in perceptions of chatbot communication in a customer service context. Socioemotional selectivity theory and research into technology acceptance suggest that older adults may differ from younger adults in motivations to use chatbots, and in perceived complexity and security of this chatbot communication. The in-depth interviews with older adults (54–81 years; N = 7) and younger adults (19–30 years; N = 7) revealed that both groups were aligned in their prime motivation: They used chatbots to get their (simple) customer queries answered in a fast and convenient manner. However, they seemed to differ in their need for additional human contact. In both age groups, there were participants for whom it was easy to communicate with chatbots, and the two groups were united in their frustrations when the chatbot did not understand and answer their queries. They were aligned as well in the difficulty they experienced in assessing the security of the chatbot. The two age groups may differ in the factors that contribute to perceived ease of use and perceived security. Directions for future research and implications for the implementation of chatbots for customer service are discussed.
22 citations
TL;DR: In this article, the authors explore how bots might be used to mediate task management for individuals and teams, and deploy a prototype bot to eight different teams of information workers to help them create, assign and keep track of tasks, all within their main communication channel.
Abstract: Effective task management is essential to successful team collaboration While the past decade has seen considerable innovation in systems that track and manage group tasks, these innovations have typically been outside of the principal communication channels: email, instant messenger, and group chat Teams formulate, discuss, refine, assign, and track the progress of their collaborative tasks over electronic communication channels, yet they must leave these channels to update their task-tracking tools, creating a source of friction and inefficiency To address this problem, we explore how bots might be used to mediate task management for individuals and teams We deploy a prototype bot to eight different teams of information workers to help them create, assign, and keep track of tasks, all within their main communication channel We derived seven insights for the design of future bots for coordinating work
22 citations
TL;DR: A way to access Arabic Web Question Answering (QA) corpus using a chatbot, without the need for sophisticated natural language processing or logical inference, is described.
Abstract: In this paper, we describe a way to access Arabic Web Question Answering (QA) corpus using a chatbot, without the need for sophisticated natural language processing or logical inference Any Natural Language (NL) interface to Question Answer (QA) system is constrained to reply with the given answers, so there is no need for NL generation to recreate well-formed answers, or for deep analysis or logical inference to map user input questions onto this logical ontology; simple (but large) set of pattern-template matching rules will suffice In previous research, this approach works properly with English and other European languages In this paper, we try to see how the same chatbot will react in terms of Arabic Web QA corpus Initial results shows that 93% of answers were correct, but because of a lot of characteristics related to Arabic language, changing Arabic questions into other forms may lead to no answers
22 citations