Backchanneling via Twitter Data for Conversational Dialogue Systems
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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