MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions

TL;DR

This paper introduces the MultiMediate challenge focused on detecting backchannels and estimating agreement in group conversations, providing new annotated data and baseline results for these tasks.

Contribution

It presents the first challenge on backchannel detection and agreement estimation, along with a new dataset of annotated group interaction recordings.

Findings

7234 backchannel instances annotated with agreement levels

Baseline results established for detection and agreement estimation tasks

Analysis of annotation data reveals insights into backchannel usage in groups

Abstract

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.