mlsanityData to 7 Oct 2026

An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering

arXiv 1912.02145 ↗First posted 4 Dec 2019cs.CLcited by 22

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Abstract (arXiv)

To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation. We find a simple negative sampling technique to be particularly effective, even though it is typically used for datasets that include unanswerable questions, such as SQuAD 2.0. When applied in conjunction with per-domain sampling, our XLNet (Yang et al., 2019)-based submission achieved the second best Exact Match and F1 in the MRQA leaderboard competition.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2019.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 6; 2024: 2.

22 papers.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 349
  3. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  4. 2022Prompting GPT-3 To Be Reliablecited by 258
  5. 2023A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicitycited by 180
  6. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  7. 2023LongForm: Effective Instruction Tuning with Reverse Instructionscited by 47
  8. 2022Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustnesscited by 23
  9. 2023Flesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language Modelscited by 17
  10. 2021Single-dataset Experts for Multi-dataset Question Answeringcited by 15
  11. 2022Active Learning Over Multiple Domains in Natural Language Taskscited by 11
  12. 2022Augmenting Multi-Turn Text-to-SQL Datasets with Self-Playcited by 9
10 more
  1. 2021Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Readingcited by 7
  2. 2022Improved Text Classification via Test-Time Augmentationcited by 6
  3. 2022When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentationcited by 5
  4. 2022Data Augmentation for Biomedical Factoid Question Answeringcited by 5
  5. 2023Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answeringcited by 5
  6. 2022QAGAN: Adversarial Approach To Learning Domain Invariant Language Featurescited by 2
  7. 2022ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering Systemcited by 1
  8. 2023Controllable Data Augmentation for Context-Dependent Text-to-SQLcited by 1
  9. 2024QOG:Question and Options Generation based on Language Modelcited by 1
  10. 2023UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Postscited by 0