  Dialog acts reveal the intention behind the uttered words. Thus, their
automatic recognition is important for a dialog system trying to understand its
conversational partner. The study presented in this article approaches that
task on the DIHANA corpus, whose three-level dialog act annotation scheme poses
problems which have not been explored in recent studies. In addition to the
hierarchical problem, the two lower levels pose multi-label classification
problems. Furthermore, each level in the hierarchy refers to a different aspect
concerning the intention of the speaker both in terms of the structure of the
dialog and the task. Also, since its dialogs are in Spanish, it allows us to
assess whether the state-of-the-art approaches on English data generalize to a
different language. More specifically, we compare the performance of different
segment representation approaches focusing on both sequences and patterns of
words and assess the importance of the dialog history and the relations between
the multiple levels of the hierarchy. Concerning the single-label
classification problem posed by the top level, we show that the conclusions
drawn on English data also hold on Spanish data. Furthermore, we show that the
approaches can be adapted to multi-label scenarios. Finally, by hierarchically
combining the best classifiers for each level, we achieve the best results
reported for this corpus.

Computation and Language