  Recent work in Dialogue Act classification has treated the task as a sequence
labeling problem using hierarchical deep neural networks. We build on this
prior work by leveraging the effectiveness of a context-aware self-attention
mechanism coupled with a hierarchical recurrent neural network. We conduct
extensive evaluations on standard Dialogue Act classification datasets and show
significant improvement over state-of-the-art results on the Switchboard
Dialogue Act (SwDA) Corpus. We also investigate the impact of different
utterance-level representation learning methods and show that our method is
effective at capturing utterance-level semantic text representations while
maintaining high accuracy.
