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Front. Inform. Technol. Electron. Eng.  2018, Vol. 19 Issue (1): 10-26    
    
From Eliza to XiaoIce: challenges and opportunities with social chatbots
Heung-yeung SHUM, Xiao-dong HE, Di LI
Microsoft Corporation, Redmond, WA 98052, USA
From Eliza to XiaoIce: challenges and opportunities with social chatbots
Heung-yeung SHUM, Xiao-dong HE, Di LI
Microsoft Corporation, Redmond, WA 98052, USA
 全文: PDF 
摘要： Conversational systems have come a long way since their inception in the 1960s. After decades of research and de-
velopment, we have seen progress from Eliza and Parry in the 1960s and 1970s, to task-completion systems as in the Defense
Advanced Research Projects Agency (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 con-
versations with appropriate interpersonal responses. As we become the first generation of humans ever living with artificial in-
telligenc (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.
关键词： Conversational system;  Social Chatbot;  Intelligent personal assistant;  Artificial intelligence;  XiaoIce    
Abstract: Conversational systems have come a long way since their inception in the 1960s. After decades of research and de-
velopment, we have seen progress from Eliza and Parry in the 1960s and 1970s, to task-completion systems as in the Defense
Advanced Research Projects Agency (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 con-
versations with appropriate interpersonal responses. As we become the first generation of humans ever living with artificial in-
telligenc (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.
Key words: Conversational system    Social Chatbot    Intelligent personal assistant    Artificial intelligence    XiaoIce
收稿日期: 2017-12-10 出版日期: 2019-06-06
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Heung-yeung SHUM
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引用本文:

Heung-yeung SHUM, Xiao-dong HE, Di LI . From Eliza to XiaoIce: challenges and opportunities with social chatbots. Front. Inform. Technol. Electron. Eng., 2018, 19(1): 10-26.

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[1] Bin YU, Karl KUMBIER. Artificial intelligence and statistics[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(1): 6-9.