Dialogue Act Classification with Context-Aware Self-Attention
This work addresses dialogue act classification for conversational AI systems, representing an incremental advance over prior hierarchical deep learning approaches.
The authors tackled dialogue act classification by combining context-aware self-attention with hierarchical recurrent neural networks, achieving significant improvements over state-of-the-art results on the Switchboard Dialogue Act Corpus.
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.