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Published in:
Automatic Speech Recognition Based on Neural Networks Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Backchanneling via Twitter Data for Conversational Dialogue Systems

Authors : Michimasa Inaba, Kenichi Takahashi

Published in: Speech and Computer

Publisher: Springer International Publishing

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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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previous chapter Automatic Summarization of Highly Spontaneous Speech
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Metadata
Title
Backchanneling via Twitter Data for Conversational Dialogue Systems
Authors
Michimasa Inaba
Kenichi Takahashi
Copyright Year
2016
Book
Speech and Computer

Print ISBN: 978-3-319-43957-0

Electronic ISBN: 978-3-319-43958-7

Copyright Year: 2016

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

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