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Dialogue Act Classification with Context-Aware Self-Attention

arXiv 1904.02594 ↗First posted 4 Apr 2019cs.CLcited by 26

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Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2019.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 7; 2024: 1; 2025: 5; 2026: 1.

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  1. 2023Is ChatGPT Equipped with Emotional Dialogue Capabilities?cited by 54
  2. 2021Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversationscited by 31
  3. 2021RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversationscited by 18
  4. 2021Speaker Turn Modeling for Dialogue Act Classificationcited by 18
  5. 2024Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representationscited by 18
  6. 2022DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognitioncited by 9
  7. 2022Towards End-to-End Integration of Dialog History for Improved Spoken Language Understandingcited by 8
  8. 2023Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understandingcited by 7
  9. 2023Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attentioncited by 6
  10. 2025Multi-Party Conversational Agents: A Surveycited by 6
  11. 2021What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognitioncited by 5
  12. 2021A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysiscited by 4
14 more
  1. 2023Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog Historycited by 3
  2. 2025BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Actscited by 2
  3. 2021Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documentscited by 1
  4. 2022Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generationcited by 1
  5. 2022Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approachcited by 1
  6. 2023When to generate hedges in peer-tutoring interactionscited by 1
  7. 2025C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Datasetcited by 1
  8. 2021Hierarchical Knowledge Distillation for Dialogue Sequence Labelingcited by 0
  9. 2022Causal Inference for Chatting Handoffcited by 0
  10. 2023SSR-TA: Sequence to Sequence based expert recurrent recommendation for ticket automationcited by 0
  11. 2023InterviewBot: Real-Time End-to-End Dialogue System to Interview Students for College Admissioncited by 0
  12. 2025Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juriescited by 0
  13. 2025From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understandingcited by 0
  14. 2026S-MARC: Causal Streaming Reasoning for Full-Duplex Conversational Behavior Modelingcited by 0