mlsanityData to 7 Oct 2026

Data Augmentation for Conversational AI

arXiv 2309.04739 ↗First posted 9 Sep 2023cs.CL cs.IRcited by 3

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Abstract (arXiv)

Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 45 of its 65 references that are in the map. First posted on arXiv 9 Sep 2023.

Builds on

45 of 65 references.

References · 45

  1. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  2. 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 426
  3. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  4. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 198
  5. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  6. 2018QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehensioncited by 164
  7. 2019Good-Enough Compositional Data Augmentationcited by 158
  8. 2019Synthetic QA Corpora Generation with Roundtrip Consistencycited by 143
  9. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  10. 2016Data Recombination for Neural Semantic Parsingcited by 111
  11. 2020Training Question Answering Models From Synthetic Datacited by 95
  12. 2018Building a Conversational Agent Overnight with Dialogue Self-Playcited by 70
33 more
  1. 2019Target-Guided Open-Domain Conversationcited by 66
  2. 2019Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Modelscited by 53
  3. 2019Soft Contextual Data Augmentation for Neural Machine Translationcited by 49
  4. 2020Generative Data Augmentation for Commonsense Reasoningcited by 49
  5. 2020Paraphrase Augmented Task-Oriented Dialog Generationcited by 43
  6. 2020CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackerscited by 43
  7. 2022Controllable Dialogue Simulation with In-Context Learningcited by 42
  8. 2021SSMix: Saliency-Based Span Mixup for Text Classificationcited by 39
  9. 2022Conversational Information Seekingcited by 36
  10. 2021Transferable Dialogue Systems and User Simulatorscited by 35
  11. 2021OTTers: One-turn Topic Transitions for Open-Domain Dialoguecited by 29
  12. 2020Snippext: Semi-supervised Opinion Mining with Augmented Datacited by 26
  13. 2020Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructionscited by 25
  14. 2020Keyword-Guided Neural Conversational Modelcited by 21
  15. 2019Interconnected Question Generation with Coreference Alignment and Conversation Flow Modelingcited by 20
  16. 2020Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Datacited by 20
  17. 2020Multilingual Transfer Learning for QA Using Translation as Data Augmentationcited by 20
  18. 2021ChainCQG: Flow-Aware Conversational Question Generationcited by 19
  19. 2022LAD: Language Models as Data for Zero-Shot Dialogcited by 18
  20. 2022Generating Information-Seeking Conversations from Unlabeled Documentscited by 17
  21. 2021NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulationcited by 16
  22. 2020Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversationcited by 15
  23. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  24. 2023Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialoguecited by 14
  25. 2019Dialog State Tracking with Reinforced Data Augmentationcited by 10
  26. 2021DG2: Data Augmentation Through Document Grounded Dialogue Generationcited by 10
  27. 2020Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentationcited by 9
  28. 2022Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentationcited by 8
  29. 2022CoHS-CQG: Context and History Selection for Conversational Question Generationcited by 8
  30. 2022Target-Guided Open-Domain Conversation Planningcited by 8
  31. 2022Conversational QA Dataset Generation with Answer Revisioncited by 5
  32. 2022DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modelingcited by 3
  33. 2022Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questionscited by 2

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledgecited by 49
  2. 2024From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classificationcited by 2
  3. 2025PromptAug: Fine-grained Conflict Classification Using Data Augmentationcited by 0