  Recent works have proposed neural models for dialog act classification in
spoken dialogs. However, they have not explored the role and the usefulness of
acoustic information. We propose a neural model that processes both lexical and
acoustic features for classification. Our results on two benchmark datasets
reveal that acoustic features are helpful in improving the overall accuracy.
Finally, a deeper analysis shows that acoustic features are valuable in three
cases: when a dialog act has sufficient data, when lexical information is
limited and when strong lexical cues are not present.
