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A Neural Conversational Model

arXiv 1506.05869 ↗First posted 19 Jun 2015cs.CLcited by 195

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Abstract (arXiv)

Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary results suggest that, despite optimizing the wrong objective function, the model is able to converse well. It is able extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domain-specific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model.

Key facts. Cited by 195 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2015.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 79; 2022: 49; 2023: 32; 2024: 19; 2025: 7; 2026: 8.

195 papers.

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  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  2. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  3. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  4. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  5. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  6. 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
  7. 2022A Survey on Retrieval-Augmented Text Generationcited by 157
  8. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  9. 2022Generative Spoken Dialogue Language Modelingcited by 125
  10. 2021Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Groundingcited by 106
  11. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memorycited by 103
  12. 2023LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Modelscited by 100
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  1. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  2. 2023Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversationscited by 88
  3. 2021Anticipating Safety Issues in E2E Conversational AI: Framework and Toolingcited by 77
  4. 2023Questioning the Survey Responses of Large Language Modelscited by 70
  5. 2022Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agentscited by 62
  6. 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Surveycited by 56
  7. 2022Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generationcited by 47
  8. 2024CiteME: Can Language Models Accurately Cite Scientific Claims?cited by 45
  9. 2022Controllable Text Generation with Neurally-Decomposed Oraclecited by 41
  10. 2021One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profilescited by 39
  11. 2022DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generationcited by 35
  12. 2023Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphscited by 35
  13. 2021EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Trainingcited by 34
  14. 2024SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generationcited by 33
  15. 2021Multimodal Dialogue Response Generationcited by 32
  16. 2021ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generationcited by 31
  17. 2024Measuring and Reducing LLM Hallucination without Gold-Standard Answerscited by 31
  18. 2021Defending Against Backdoor Attacks in Natural Language Generationcited by 30
  19. 2021Commonsense-Focused Dialogues for Response Generation: An Empirical Studycited by 30
  20. 2022A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learningcited by 30
  21. 2021OTTers: One-turn Topic Transitions for Open-Domain Dialoguecited by 29
  22. 2023WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpuscited by 29
  23. 2021Fusing task-oriented and open-domain dialogues in conversational agentscited by 28
  24. 2021Automatic Evaluation and Moderation of Open-domain Dialogue Systemscited by 28
  25. 2022Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methodscited by 27
  26. 2025SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakescited by 27
  27. 2022A Literature Survey of Recent Advances in Chatbotscited by 26
  28. 2024Building Better AI Agents: A Provocation on the Utilisation of Persona in LLM-based Conversational Agentscited by 26
  29. 2021DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialoguescited by 25
  30. 2025SALMONN-omni: A Standalone Speech LLM without Codec Injection for Full-duplex Conversationcited by 25
  31. 2021Focused Attention Improves Document-Grounded Generationcited by 24
  32. 2021Semantic Representation for Dialogue Modelingcited by 24
  33. 2024SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakescited by 24
  34. 2023Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedbackcited by 23
  35. 2023A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Modelscited by 23
  36. 2024Controllable Context Sensitivity and the Knob Behind Itcited by 22
  37. 2021Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interactioncited by 21
  38. 2021EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systemscited by 20

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