Papers › Dialogue Act Classification with Context-Aware Self-Attention
Dialogue Act Classification with Context-Aware Self-Attention
Vipul Raheja, Joel Tetreault
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dialogue Act Classification | ICSI Meeting Recorder Dialog Act (MRDA) corpus | Bi-RNN + Self-Attention + Context | Accuracy | 91.1 | #6 of 8 | Archive leaderboard | report |
| Dialogue Act Classification | Switchboard corpus | Bi-RNN + Self-Attention + Context | Accuracy | 82.9 | #3 of 11 | Archive leaderboard | report |
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