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Synthetic QA Corpora Generation with Roundtrip Consistency

arXiv 1906.05416 ↗First posted 12 Jun 2019cs.CLcited by 143

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Abstract (arXiv)

We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2.

Key facts. Cited by 143 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Citing papers per year: 2021: 32; 2022: 40; 2023: 32; 2024: 18; 2025: 14; 2026: 7.

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  1. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  2. 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 361
  3. 2021ScanQA: 3D Question Answering for Spatial Scene Understandingcited by 340
  4. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 233
  5. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 198
  6. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
  7. 2022StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Modelscited by 123
  8. 2021Improving Question Answering Model Robustness with Synthetic Adversarial Data Generationcited by 77
  9. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
  10. 2022All You May Need for VQA are Image Captionscited by 65
  11. 2021Few-Shot Question Answering by Pretraining Span Selectioncited by 62
  12. 2021Domain-matched Pre-training Tasks for Dense Retrievalcited by 53
38 more
  1. 2021NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learnedcited by 52
  2. 2024Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrievalcited by 50
  3. 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledgecited by 49
  4. 2021One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrievalcited by 46
  5. 2022Conditional Generation with a Question-Answering Blueprintcited by 44
  6. 2021Zero-shot Fact Verification by Claim Generationcited by 42
  7. 2021Generate, Annotate, and Learn: NLP with Synthetic Textcited by 42
  8. 2022Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inferencecited by 40
  9. 2021Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skillscited by 39
  10. 2024BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrieverscited by 38
  11. 2021Capturing Row and Column Semantics in Transformer Based Question Answering over Tablescited by 31
  12. 2021Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistantscited by 31
  13. 2023Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChaincited by 31
  14. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  15. 2022Learning to Answer Visual Questions from Web Videoscited by 30
  16. 2023The Trickle-down Impact of Reward (In-)consistency on RLHFcited by 29
  17. 2021Retrieval-guided Counterfactual Generation for QAcited by 28
  18. 2023Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answeringcited by 28
  19. 2021FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text modelscited by 26
  20. 2022QAmeleon: Multilingual QA with Only 5 Examplescited by 25
  21. 2024A Survey on Recent Advances in Conversational Data Generationcited by 23
  22. 2022HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarizationcited by 22
  23. 2021Improving Unsupervised Question Answering via Summarization-Informed Question Generationcited by 21
  24. 2021Contrastive Domain Adaptation for Question Answering using Limited Text Corporacited by 20
  25. 2022Improving Biomedical Information Retrieval with Neural Retrieverscited by 20
  26. 2024Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddingscited by 20
  27. 2021Question Answering Infused Pre-training of General-Purpose Contextualized Representationscited by 19
  28. 2021MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Groundingcited by 19
  29. 2022Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answeringcited by 19
  30. 2023LIQUID: A Framework for List Question Answering Dataset Generationcited by 19
  31. 2024Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Modelscited by 19
  32. 2022Generating Information-Seeking Conversations from Unlabeled Documentscited by 17
  33. 2022Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generationcited by 17
  34. 2022Socratic Pretraining: Question-Driven Pretraining for Controllable Summarizationcited by 17
  35. 2023CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibrationcited by 16
  36. 2021QACE: Asking Questions to Evaluate an Image Captioncited by 15
  37. 2023Separating form and meaning: Using self-consistency to quantify task understanding across multiple sensescited by 15
  38. 2023An Empirical Comparison of LM-based Question and Answer Generation Methodscited by 15

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