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Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots

arXiv 2004.03588 ↗First posted 7 Apr 2020cs.CLcited by 73

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Abstract (arXiv)

In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT (SA-BERT), is proposed in order to make the model aware of the speaker change information, which is an important and intrinsic property of multi-turn dialogues. Furthermore, a speaker-aware disentanglement strategy is proposed to tackle the entangled dialogues. This strategy selects a small number of most important utterances as the filtered context according to the speakers' information in them. Finally, domain adaptation is performed to incorporate the in-domain knowledge into pre-trained language models. Experiments on five public datasets show that our proposed model outperforms the present models on all metrics by large margins and achieves new state-of-the-art performances for multi-turn response selection.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2020.

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Citing papers per year: 2021: 24; 2022: 19; 2023: 19; 2024: 4; 2025: 7.

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  1. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
  2. 2021MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understandingcited by 36
  3. 2023LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streamingcited by 27
  4. 2021Graph Based Network with Contextualized Representations of Turns in Dialoguecited by 26
  5. 2022CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AIcited by 25
  6. 2021On the Calibration and Uncertainty of Neural Learning to Rank Modelscited by 24
  7. 2021Structural Pre-training for Dialogue Comprehensioncited by 20
  8. 2023Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLMcited by 18
  9. 2021Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluationcited by 16
  10. 2022HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversationscited by 16
  11. 2021Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbotscited by 15
  12. 2021Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbotscited by 14
38 more
  1. 2021Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehensioncited by 13
  2. 2021Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehensioncited by 13
  3. 2021Advances in Multi-turn Dialogue Comprehension: A Surveycited by 12
  4. 2021Exploring Dense Retrieval for Dialogue Response Selectioncited by 11
  5. 2024SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluationcited by 11
  6. 2023GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understandingcited by 10
  7. 2021Dialogue-oriented Pre-trainingcited by 9
  8. 2021Integrating Dialog History into End-to-End Spoken Language Understanding Systemscited by 9
  9. 2021Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversationscited by 9
  10. 2023P5: Plug-and-Play Persona Prompting for Personalized Response Selectioncited by 8
  11. 2021Structural Characterization for Dialogue Disentanglementcited by 7
  12. 2023Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarizationcited by 7
  13. 2022Persona-Based Conversational AI: State of the Art and Challengescited by 6
  14. 2023Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attentioncited by 6
  15. 2025Multi-Party Conversational Agents: A Surveycited by 6
  16. 2023MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generationcited by 5
  17. 2021Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systemscited by 4
  18. 2021Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learningcited by 4
  19. 2022A Systematic Evaluation of Response Selection for Open Domain Dialoguecited by 4
  20. 2023Pre-training Multi-party Dialogue Models with Latent Discourse Inferencecited by 4
  21. 2023Multi-turn Dialogue Comprehension from a Topic-aware Perspectivecited by 4
  22. 2023Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialoguecited by 4
  23. 2024The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trendscited by 4
  24. 2021Challenging Instances are Worth Learning: Generating Valuable Negative Samples for Response Selection Trainingcited by 3
  25. 2021FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoningcited by 3
  26. 2021Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learningcited by 3
  27. 2022DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modelingcited by 3
  28. 2022DialGuide: Aligning Dialogue Model Behavior with Developer Guidelinescited by 3
  29. 2021Does Dialog Length matter for Next Response Selection task? An Empirical Studycited by 2
  30. 2021Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systemscited by 2
  31. 2022Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialoguecited by 2
  32. 2022Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selectioncited by 2
  33. 2022IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selectioncited by 2
  34. 2023Dramatic Conversation Disentanglementcited by 2
  35. 2023Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systemscited by 2
  36. 2023Incomplete Utterance Rewriting as Sequential Greedy Taggingcited by 2
  37. 2024Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluationcited by 2
  38. 2025M3HG: Multimodal, Multi-scale, and Multi-type Node Heterogeneous Graph for Emotion Cause Triplet Extraction in Conversationscited by 2

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