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.
@article{arxiv.1904.02594,
title = {Dialogue Act Classification with Context-Aware Self-Attention},
author = {Vipul Raheja and Joel Tetreault},
journal= {arXiv preprint arXiv:1904.02594},
year = {2019}
}