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Backchanneling via Twitter Data for Conversational Dialogue Systems

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Speech and Computer (SPECOM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9811))

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Abstract

Backchanneling plays a crucial role in human-to-human communication. In this study, we propose a method for generating a rich variety of backchanneling, which is not just limited to simple “hm” or “sure” responses, to realize smooth communication in conversational dialogue systems. We formulate the problem of what the backchanneling generation function should return for given user inputs as a multi-class classification problem and determine a suitable class using a recurrent neural network with a long short-term memory. Training data for our model comprised pairs of tweets and replies acquired from Twitter. Experimental results demonstrated that our method can appropriately select backchannels to given inputs and significantly outperform baseline methods.

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Author information

Authors and Affiliations

  1. Hiroshima City University, 3-4-1 Ozukahigashi, Asaminami-ku, Hiroshima, Japan

    Michimasa Inaba & Kenichi Takahashi

Authors
  1. Michimasa Inaba
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  2. Kenichi Takahashi
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Corresponding author

Correspondence to Michimasa Inaba .

Editor information

Editors and Affiliations

  1. SPIIRAS , Saint-Petersburg, Russia

    Andrey Ronzhin

  2. Moscow State Linguistic University , Moscow, Russia

    Rodmonga Potapova

  3. Budapest University of Technology and Economics, Budapest, Hungary

    Géza Németh

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Inaba, M., Takahashi, K. (2016). Backchanneling via Twitter Data for Conversational Dialogue Systems. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_17

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  • DOI: https://doi.org/10.1007/978-3-319-43958-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43957-0

  • Online ISBN: 978-3-319-43958-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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