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.
Papers published on a yearly basis
Papers
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19 Nov 2019TL;DR: The paper describes how state-of-the-art chatbot technology can enable a dialog between the user and the website, proposes a reference architecture for the automated inference of site-specific chatbots able to mediate between theuser and the websites, and discusses open challenges and research questions.
Abstract: This paper lays the foundation for a new delivery paradigm for web-accessible content and functionality, i.e., conversational interaction. Instead of asking users to read text, click through links and type on the keyboard, the vision is to enable users to “speak to a website” and to obtain natural language, spoken feedback. The paper describes how state-of-the-art chatbot technology can enable a dialog between the user and the website, proposes a reference architecture for the automated inference of site-specific chatbots able to mediate between the user and the website, and discusses open challenges and research questions. The envisioned, bidirectional dialog paradigm advances current screen reader technology and aims to benefit both regular users in eyes-free usage scenarios as well as visually impaired users in everyday scenarios.
16 citations
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TL;DR: In this article, the authors study the differences in user satisfaction with a chatbot system vis-a-vis a menu-based interface system, and identify factors that influence user satisfaction.
16 citations
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04 Mar 2008TL;DR: The Graphical Artificial Intelligence Markup Language is presented, an extension of AIML allowing merging of verbal and graphical interaction modalities and a chatbot system, Graphbot, is also presented that is able to support this language.
Abstract: One of the aims of the research in the field of the human-computer interaction is the design of a natural and intuitive interaction modalities In particular, many efforts have been devoted in the development of systems able to interact with the user in natural language Chatbots are the classical interfaces for natural language interaction Such systems can be very sophisticated, including support for 3D avatars and speech analysis and synthesis However, all of them present only a text area allowing the user to input her sentences No doubt, an interaction involving also the natural language can increase the comfort of the user with respect to common interfaces using only graphical widgets However, multi-modal communication must be preferred in all those situations when the user and the system have a tight interaction Typical examples are cultural heritages applications (intelligent museum guides, picture browsing) or systems presenting to the user an information integrated from different sources as in the case of the iGoogle (TM) interface In this work we present the Graphical Artificial Intelligence Markup Language, an extension of AIML allowing merging of verbal and graphical interaction modalities A chatbot system, Graphbot, is also presented that is able to support this language The language is able to define personalized interface patterns that are the most suitable ones in relation to the type of data exchanged between the user and the system during the dialogue
16 citations
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TL;DR: In insights gathered from the design and implementation of an SMS chatbot-based virtual assistant to hotel guests in London, the author discusses challenges and first outcomes and makes recommendations based on this experience toward best practices for approaching the design of chatbots in the customer service domain.
Abstract: This article shares insights gathered from the design and implementation of an SMS chatbot-based virtual assistant to hotel guests in London. The author discusses challenges and first outcomes, and makes recommendations based on this experience toward best practices for approaching the design of chatbots in the customer service domain.
15 citations
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01 Nov 2019TL;DR: Methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue are presented.
Abstract: Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.). However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive.
15 citations