  ISO 24617-2, the standard for dialog act annotation, defines a hierarchically
organized set of general-purpose communicative functions. The automatic
recognition of these functions, although practically unexplored, is relevant
for a dialog system, since they provide cues regarding the intention behind the
segments and how they should be interpreted. We explore the recognition of
general-purpose communicative functions in the DialogBank, which is a reference
set of dialogs annotated according to this standard. To do so, we propose
adaptations of existing approaches to flat dialog act recognition that allow
them to deal with the hierarchical classification problem. More specifically,
we propose the use of a hierarchical network with cascading outputs and maximum
a posteriori path estimation to predict the communicative function at each
level of the hierarchy, preserve the dependencies between the functions in the
path, and decide at which level to stop. Furthermore, since the amount of
dialogs in the DialogBank is reduced, we rely on transfer learning processes to
reduce overfitting and improve performance. The results of our experiments show
that the hierarchical approach outperforms a flat one and that each of its
components plays an important role towards the recognition of general-purpose
communicative functions.
