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

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.

arXiv commentACM Multimedia 2022

References in corpus (4)

• Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour
• Dialogue Act Classification with Context-Aware Self-Attention
• Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation
• Exploring Semi-Supervised Learning for Predicting Listener Backchannels

Cited by in corpus (4)

• Data Augmentation for Human Behavior Analysis in Multi-Person Conversations
• MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
• Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement
• MultiMediate'24: Multi-Domain Engagement Estimation