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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
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Journal ArticleDOI
TL;DR: This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted.
Abstract: Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prize-winning Chatbots.

329 citations

Journal ArticleDOI
TL;DR: By all accounts, 2016 is the year of the chatbot, and some commentators take the view that chatbot technology will be so disruptive that it will eliminate the need for websites and apps.
Abstract: By all accounts, 2016 is the year of the chatbot. Some commentators take the view that chatbot technology will be so disruptive that it will eliminate the need for websites and apps. But chatbots have a long history. So what's new, and what's different this time? And is there an opportunity here to improve how our industry does technology transfer?

321 citations

Proceedings ArticleDOI
07 Mar 2017
TL;DR: This paper studies conversational approaches to information retrieval, presenting a theory and model of information interaction in a chat setting, and shows that while theoretical, the model could be practically implemented to satisfy the desirable properties presented.
Abstract: This paper studies conversational approaches to information retrieval, presenting a theory and model of information interaction in a chat setting. In particular, we consider the question of what properties would be desirable for a conversational information retrieval system so that the system can allow users to answer a variety of information needs in a natural and efficient manner. We study past work on human conversations, and propose a small set of properties that taken together could measure the extent to which a system is conversational. Following this, we present a theoretical model of a conversational system that implements the properties. We describe how this system could be implemented, making the action space of an conversational search agent explicit. Our analysis of this model shows that while theoretical, the model could be practically implemented to satisfy the desirable properties presented. In doing so, we show that the properties are also feasible.

319 citations

Journal ArticleDOI
TL;DR: Chatbot identity disclosure negatively affects customer purchases because customers perceive the disclosed bot as less knowledgeable and less empathetic.
Abstract: Chatbot identity disclosure negatively affects customer purchases because customers perceive the disclosed bot as less knowledgeable and less empathetic.

311 citations

Posted Content
TL;DR: In this article, the authors define the success metric for social chatbots as conversation-turns per session (CPS) and use XiaoIce as an illustrative example to show how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses.
Abstract: Conversational systems have come a long way since their inception in the 1960s. After decades of research and development, we've seen progress from Eliza and Parry in the 60's and 70's, to task-completion systems as in the DARPA Communicator program in the 2000s, to intelligent personal assistants such as Siri in the 2010s, to today's social chatbots like XiaoIce. Social chatbots' appeal lies not only in their ability to respond to users' diverse requests, but also in being able to establish an emotional connection with users. The latter is done by satisfying users' need for communication, affection, as well as social belonging. To further the advancement and adoption of social chatbots, their design must focus on user engagement and take both intellectual quotient (IQ) and emotional quotient (EQ) into account. Users should want to engage with a social chatbot; as such, we define the success metric for social chatbots as conversation-turns per session (CPS). Using XiaoIce as an illustrative example, we discuss key technologies in building social chatbots from core chat to visual awareness to skills. We also show how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses. As we become the first generation of humans ever living with AI, we have a responsibility to design social chatbots to be both useful and empathetic, so they will become ubiquitous and help society as a whole.

308 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023916
20221,413
2021564
2020617
2019528
2018326