From Eliza to XiaoIce:challenges and opportunities with social chatbots

被引:9
作者
Heung-yeung SHUM [1 ]
Xiao-dong HE [1 ]
Di LI [1 ]
机构
[1] Microsoft Corporation
关键词
Conversational system; Social Chatbot; Intelligent personal assistant; Artificial intelligence; Xiao Ice;
D O I
暂无
中图分类号
TP18 [人工智能理论]; TP242 [机器人];
学科分类号
081104 ; 0812 ; 0835 ; 1111 ; 1405 ;
摘要
Conversational systems have come a long way since their inception in the 1960 s.After decades of research and development,we have seen progress from Eliza and Parry in the 1960 s and 1970 s,to task-completion systems as in the Defense Advanced Research Projects Agency(DARPA) communicator program in the 2000 s,to intelligent personal assistants such as Siri,in the 2010 s,to today’s social chatbots like Xiao Ice.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 Xiao Ice as an illustrative example,we discuss key technologies in building social chatbots from core chat to visual awareness to skills.We also show how Xiao Ice 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 artificial intelligenc(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.
引用
收藏
页码:10 / 26
页数:17
相关论文
共 9 条
  • [1] Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding
    Mesnil, Gregoire
    Dauphin, Yann
    Yao, Kaisheng
    Bengio, Yoshua
    Deng, Li
    Hakkani-Tur, Dilek
    He, Xiaodong
    Heck, Larry
    Tur, Gokhan
    Yu, Dong
    Zweig, Geoffrey
    [J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2015, 23 (03) : 530 - 539
  • [2] Partially observable Markov decision processes for spoken dialog systems[J] . Jason D. Williams,Steve Young.Computer Speech & Language . 2006 (2)
  • [3] Long short-term memory
    Hochreiter, S
    Schmidhuber, J
    [J]. NEURAL COMPUTATION, 1997, 9 (08) : 1735 - 1780
  • [4] Multilingual spoken-language understanding in the MIT Voyager system[J] . James Glass,Giovanni Flammia,David Goodine,Michael Phillips,Joseph Polifroni,Shinsuke Sakai,Stephanie Seneff,Victor Zue.Speech Communication . 1995 (1)
  • [5] LESSONS FROM A RESTRICTED TURING TEST
    SHIEBER, SM
    [J]. COMMUNICATIONS OF THE ACM, 1994, 37 (06) : 70 - 78
  • [6] ELIZA—a computer program for the study of natural language communication between man and machine[J] . Joseph Weizenbaum.Communications of the ACM . 1966 (1)
  • [7] Multiresolution recurrent neural networks:an application to dialogue response generation .2 Serban IV,Klinger T,Tesauro G,et al. . 2017
  • [8] A Framework for Automatic Human Emotion Classification Using Emotion Profiles .2 Mower,E,Mataric,M.J,Narayanan,S. Audio, Speech, and Language Processing, IEEE Transactions on . 2011
  • [9] Senti Cap:generating image descriptions with sentiments .2 Mathews A,Xie LX,He XM. Proc 30th AAAI Conf on Artificial Intelligence . 2016