Published on Thu Apr 04 2019

Dialogue Act Classification with Context-Aware Self-Attention

Vipul Raheja, Joel Tetreault

We leverage the effectiveness of a context-aware self-attention Mechanism coupled with a hierarchical recurrent neural network. We show a significant improvement over state-of-the-art results on the Switchboard Corpus. We also investigate the impact of different utterance-level representation learning methods.

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Abstract

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.