Abstract
We design an attentive listening agent which can generate flexible backchannels. In our previous work, we analyzed the morphological forms (category) of backchannels by focusing on their relationship with the syntactic structure in the preceding utterances. In this paper, based on the analysis, we conduct machine learning to predict the category of backchannels using features of the preceding utterance. At first, we annotated all acceptable backchannel categories for every backchannel occurrence and regard them as a ``correct'' label for the reference in evaluating prediction of the backchannel category. This annotation also gives a good insight on the relationship between backchannel forms. The results of the prediction suggest that we can choose appropriate backchannels depending on the preceding utterance. The proposed model improved prediction accuracy in comparison with the baseline which always outputs the most frequent morphological form of backchannels. Furthermore, evaluations by human subjects show that our method obtained a significantly higher rating than the baseline method.
