%0 Conference Proceedings %T Dialogue Act Classification with Context-Aware Self-Attention %A Raheja, Vipul %A Tetreault, Joel %Y Burstein, Jill %Y Doran, Christy %Y Solorio, Thamar %S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) %D 2019 %8 June %I Association for Computational Linguistics %C Minneapolis, Minnesota %F raheja-tetreault-2019-dialogue %X 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. %R 10.18653/v1/N19-1373 %U https://aclanthology.org/N19-1373 %U https://doi.org/10.18653/v1/N19-1373 %P 3727-3733