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The Design and Implementation of XiaoIce, an Empathetic Social Chatbot
This paper describes the development of the Microsoft XiaoIce system, th...
12/21/2018 ∙ by Li Zhou, et al. ∙
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Gunrock: A Social Bot for Complex and Engaging Long Conversations
Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by co...
10/07/2019 ∙ by Dian Yu, et al. ∙
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Challenges in Building Intelligent Open-domain Dialog Systems
There is a resurgent interest in developing intelligent open-domain dial...
05/13/2019 ∙ by Minlie Huang, et al. ∙
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Ruuh: A Deep Learning Based Conversational Social Agent
Dialogue systems and conversational agents are becoming increasingly pop...
10/22/2018 ∙ by Sonam Damani, et al. ∙
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Smart Conversational Agents for Reminiscence
In this paper we describe the requirements and early system design for a...
04/18/2018 ∙ by Svetlana Nikitina, et al. ∙
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Animo: Sharing Biosignals on a Smartwatch for Lightweight Social Connection
We present Animo, a smartwatch app that enables people to share and view...
04/12/2019 ∙ by Fannie Liu, et al. ∙
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A Pilot Study on Using an Intelligent Life-like Robot as a Companion for Elderly Individuals with Dementia and Depression
This paper presents the design, development, methodology, and the result...
12/07/2017 ∙ by Hojjat Abdollahi, et al. ∙
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From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots
01/06/2018 ∙ by Heung-Yeung Shum, et al. ∙
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Conversational systems have come a long way after decades of research and development, from Eliza and Parry in the 60's and 70's, to task-completion systems as in the ATIS project, to intelligent personal assistants such as Siri, and to today's social chatbots like XiaoIce. Social chatbots' appeal lies in not only 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 the users' essential needs for communication, affection, and social belonging. The design of social chatbots must focus on user engagement and take both intellectual quotient (IQ) and emotional quotient (EQ) into account. Users should want to engage with the 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 sense 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, social chatbots that are well-designed to be both useful and empathic will soon be ubiquitous.
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