mlsanityData to 7 Oct 2026

Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

arXiv 2010.10216 ↗First posted 20 Oct 2020cs.CL cs.AIcited by 25

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Abstract (arXiv)

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - the MultiWOZ dataset and the Persona chat dataset.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2020.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 7; 2024: 8; 2025: 2; 2026: 3.

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  1. 2023Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extractioncited by 89
  2. 2022InPars: Data Augmentation for Information Retrieval using Large Language Modelscited by 60
  3. 2023Faithful Persona-based Conversational Dataset Generation with Large Language Modelscited by 45
  4. 2021True Few-Shot Learning with Prompts -- A Real-World Perspectivecited by 38
  5. 2024A Survey on Recent Advances in Conversational Data Generationcited by 23
  6. 2025Proactive Assistant Dialogue Generation from Streaming Egocentric Videoscited by 22
  7. 2023Learning to Simulate Natural Language Feedback for Interactive Semantic Parsingcited by 19
  8. 2023User Simulation with Large Language Models for Evaluating Task-Oriented Dialoguecited by 19
  9. 2024ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Informationcited by 17
  10. 2022BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasetscited by 13
  11. 2023Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challengescited by 9
  12. 2024Multi-Document Grounded Multi-Turn Synthetic Dialog Generationcited by 7
13 more
  1. 2024Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Modelscited by 6
  2. 2024Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Trackingcited by 6
  3. 2023Data Augmentation for Conversational AIcited by 3
  4. 2023More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Rankingcited by 2
  5. 2024Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentationcited by 2
  6. 2025Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactionscited by 2
  7. 2024Edisum: Summarizing and Explaining Wikipedia Edits at Scalecited by 1
  8. 2026Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogscited by 1
  9. 2022Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddingscited by 0
  10. 2022Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialogcited by 0
  11. 2024Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groupscited by 0
  12. 2026Blackbird Language Matrices: A Framework to Investigate the Linguistic Competence of Language Modelscited by 0
  13. 2026The Significance of Style Diversity in Annotation-Free Synthetic Data Generationcited by 0