Unlike Hsueh and Moore (2007),<cite> Fernández et al. (2008)</cite> made an attempt at modelling the structure of decision-making dialogue. They designed an annotation scheme that takes account of the different roles which different utterances play in the decision-making process -for example, their scheme distinguishes between decision DAs (DDAs) which initiate a discussion by raising a topic/issue, those which propose a resolution, and those which express agreement for a proposed resolution and cause it to be accepted as a decision. The authors applied the annotation scheme to a portion of the AMI corpus, and then took what they refer to as a hierarchical classification approach in order to automatically identify decision discussions and their component DAs. Here, one binary Support Vector Machine (SVM) per DDA class hypothesized occurrences of that DDA class, and then based on the hypotheses of these socalled sub-classifiers, a super-classifier, (a further SVM), determined which regions of dialogue represented decision discussions. This approach produced better results than the kind of "flat classification" approach pursued by Hsueh and Moore (2007) where a single classifier looks for examples of a single decision-related DA class. Using manual transcripts, and a variety of lexical, utterance, speaker, DA and prosodic features for the sub-classifiers, the super-classifier's F1-score was 0.58 according to a lenient match metric.