  Developing human-like conversational agents is a prime area in HCI research
and subsumes many tasks. Predicting listener backchannels is one such
actively-researched task. While many studies have used different approaches for
backchannel prediction, they all have depended on manual annotations for a
large dataset. This is a bottleneck impacting the scalability of development.
To this end, we propose using semi-supervised techniques to automate the
process of identifying backchannels, thereby easing the annotation process. To
analyze our identification module's feasibility, we compared the backchannel
prediction models trained on (a) manually-annotated and (b) semi-supervised
labels. Quantitative analysis revealed that the proposed semi-supervised
approach could attain 95% of the former's performance. Our user-study findings
revealed that almost 60% of the participants found the backchannel responses
predicted by the proposed model more natural. Finally, we also analyzed the
impact of personality on the type of backchannel signals and validated our
findings in the user-study.
