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ISO 24617-2, the standard for dialog act annotation, defines a hierarchicallyorganized set of general-purpose communicative functions. The automaticrecognition of these functions, although practically unexplored, is relevantfor a dialog system, since they provide cues regarding the intention behind thesegments and how they should be interpreted. We explore the recognition ofgeneral-purpose communicative functions in the DialogBank, which is a referenceset of dialogs annotated according to this standard. To do so, we proposeadaptations of existing approaches to flat dialog act recognition that allowthem to deal with the hierarchical classification problem. More specifically,we propose the use of a hierarchical network with cascading outputs and maximuma posteriori path estimation to predict the communicative function at eachlevel of the hierarchy, preserve the dependencies between the functions in thepath, and decide at which level to stop. Furthermore, since the amount ofdialogs in the DialogBank is reduced, we rely on transfer learning processes toreduce overfitting and improve performance. The results of our experiments showthat the hierarchical approach outperforms a flat one and that each of itscomponents plays an important role towards the recognition of general-purposecommunicative functions.

Automatic Recognition of the General-Purpose Communicative Functions defined by the ISO 24617-2 Standard for Dialog Act Annotation