Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroupInteraction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.
@article{arxiv.2209.09578,
title = {MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions},
author = {Philipp Müller and Michael Dietz and Dominik Schiller and Dominike Thomas and Hali Lindsay and Patrick Gebhard and Elisabeth André and Andreas Bulling},
journal= {arXiv preprint arXiv:2209.09578},
year = {2022}
}