From Eliza to XiaoIce:challenges and opportunities with social chatbots
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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.
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.
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.
引文
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Krizhevsky A,Sutskever I,Hinton GE,2012.Image Net classification with deep convolutional neural networks.Proc25th Int Conf on Neural Information Processing Systems,p.1097-1105.
Levin E,Narayanan S,Pieraccini R,et al.,2000.The ATT-DARPA ommunicator mixed-initiative spoken dialog system.6th Int Conf on Spoken Language Processing.
Li JW,Galley M,Brockett C,et al.,2016.A persona-based neural conversation model.Proc 54th Annual Meeting of the Association for Computational Linguistics,p.944-1003.
Li X,Mou LL,Yan R,et al.,2016.Stalematebreaker:a proactive content-introducing approach to automatic humancomputer conversation.Proc 25th Int Joint Conf on Artificial Intelligence,p.2845-2851.
Liu XD,Gao JF,He XD,et al.,2015.Representation learning using multi-task deep neural networks for semantic classification and information retrieval.Proc Annual Conf on North American Chapter of the ACL,p.912-921.
Lu ZD,Li H,2013.A deep architecture for matching short texts.Proc Int Conf on Neural Information Processing Systems,p.1367-1375.
Maslow AH,1943.A theory of human motivation.Psychol Rev,50(4):370-396.
Mathews A,Xie LX,He XM,2016.Senti Cap:generating image descriptions with sentiments.Proc 30th AAAI Conf on Artificial Intelligence,p.3574-3580.
Mesnil G,He X,Deng L,et al.,2013.Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding.Interspeech,p.3771-3775.
Mesnil G,Dauphin Y,Yao KS,et al.,2015.Using recurrent neural networks for slot filling in spoken language understanding.IEEE/ACM Trans Audio Speech Lang Process,23(3):530-539.https://doi.org/10.1109/TASLP.2014.2383614
Mikolov T,Sutskever I,Chen K,et al.,2013.Distributed representations of words and phrases and their compositionality.Proc 26th Int Conf on Neural Information Processing Systems,p.3111-3119.
Mower E,Matari?MJ,Narayanan S,2011.A framework for automatic human emotion classification using emotion profiles.IEEE Trans Audio Speech Lang Process,19(5):1057-1070.https://doi.org/10.1109/TASL.2010.2076804
Murphy KR,2007.A critique of emotional intelligence:what are the problems and how can they be fixed?Pers Psychol-,60(1):235-238.https://doi.org/10.1111/j.1744-6570.2007.00071_2.x
Price PJ,1990.Evaluation of spoken language systems:the ATIS domain.Proc Workshop on Speech and Natural Language,p.91-95.https://doi.org/10.3115/116580.116612
Qian Y,Fan YC,Hu WP,et al.,2014.On the training aspects of deep neural network(DNN)for parametric TTSsynthesis.Proc IEEE Int Conf on Acoustics,Speech and Signal Processing,p.3829-3833.https://doi.org/10.1109/ICASSP.2014.6854318
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Rudnicky AI,Thayer EH,Constantinides PC,et al.,1999.Creating natural dialogs in the Carnegie Mellon communicator system.6th European Conf on Speech Communication and Technology.
Sarikaya R,2017.The technology behind personal digital assistants-an overview of the system architecture and key components.IEEE Signal Process Mag,34(1):67-81.https://doi.org/10.1109/MSP.2016.2617341
Sarikaya R,Crook PA,Marin A,et al.,2016.An overview of end-to-end language understanding and dialog management for personal digital assistants.Proc IEEE Spoken Language Technology Workshop,p.391-397.https://doi.org/10.1109/SLT.2016.7846294
Seneff S,Hurley E,Lau R,et al.,1998.Galaxy-II:a reference architecture for conversational system development.5th Int Conf on Spoken Language Processing.
Serban IV,Klinger T,Tesauro G,et al.,2017.Multiresolution recurrent neural networks:an application to dialogue response generation.AAAI,p.3288-3294.
Shawar BA,Atwell E,2007.Different measurements metrics to evaluate a chatbot system.Proc Workshop on Bridging the Gap:Academic and Industrial Research in Dialog Technologies,p.89-96.
Shieber SM,1994.Lessons from a restricted Turing test.Commun ACM,37(6):70-78.https://doi.org/10.1145/175208.175217
Socher R,Perelygin A,Wu JY,et al.,2013.Recursive deep models for semantic compositionality over a sentiment treebank.Proc Conf on Empirical Methods in Natural Language Processing,p.1631-1642.
Song R,2018.Image to poetry by cross-modality understanding with unpaired data.Personal Communication.
Sordoni A,Galley M,Auli M,et al.,2015.A neural network approach to context-sensitive generation of conversational responses.Proc Annual Conf on North American Chapter of the ACL,p.196-205.
Sutskever I,Vinyals O,Le QVV,2014.Sequence to sequence learning with neural networks.NIPS,p.1-9.https://doi.org/10.1007/s10107-014-0839-0
Tokuhisa R,Inui K,Matsumoto Y,2008.Emotion classification using massive examples extracted from the web.Proc22nd Int Conf on Computational Linguistics,p.881-888.
Tur G,de Mori R,2011.Spoken Language Understanding:Systems for Extracting Semantic Information from Speech.John Wiley and Sons,New York,NY.
Tur G,Deng L,2011.Intent determination and spoken utterance classification.In:Tur G,de Mori R(Eds.),Spoken Language Understanding:Systems for Extracting Semantic Information from Speech.John Wiley and Sons,New York,NY.
Turing A,1950.Computing machinery and intelligence.Mind,59:433-460.
van den Oord A,Dieleman S,Zen HG,et al.,2016.Wave Net:a generative model for raw audio.9th ISCA Speech Synthesis Workshop,p.125.
Vinyals O,Le QV,2015.A neural conversational model.Proc31st Int Conf on Machine Learning.
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