Abstract
This paper focuses on a lesser studied multiparty meetings processing task of argument diagramming. Argument diagramming aims at tagging the utterances and their relationships to represent the flow and structure of reasoning in conversations, especially in discussions and arguments. In this work, we tackle the problem of automatically assigning node types to user utterances using several lexical and prosodic features. We performed experiments using the AMI Meeting Corpus annotated according to the the Twente Argumentation Schema. Our results indicate that while lexical and prosodic features both provide orthogonal information for this task, using a cascaded approach, eliminating backchannel utterances improves the performance. With this final approach, when all features are used, we achieve about 9% relatively better error rates than a simpler classifier based on only lexical features.
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