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

arXiv: https://arxiv.org/abs/1912.02145 · HTML: https://mlsanity.com/paper/1912.02145/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 22 of 22)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Entity-Based Knowledge Conflicts in Question Answering](https://mlsanity.com/paper/2109.05052/) (2021)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
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- [A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity](https://mlsanity.com/paper/2305.13169/) (2023)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [LongForm: Effective Instruction Tuning with Reverse Instructions](https://mlsanity.com/paper/2304.08460/) (2023)
- [Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness](https://mlsanity.com/paper/2203.07653/) (2022)
- [Flesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language Models](https://mlsanity.com/paper/2309.05454/) (2023)
- [Single-dataset Experts for Multi-dataset Question Answering](https://mlsanity.com/paper/2109.13880/) (2021)
- [Active Learning Over Multiple Domains in Natural Language Tasks](https://mlsanity.com/paper/2202.00254/) (2022)
- [Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play](https://mlsanity.com/paper/2210.12096/) (2022)
- [Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading](https://mlsanity.com/paper/2106.04134/) (2021)
- [Improved Text Classification via Test-Time Augmentation](https://mlsanity.com/paper/2206.13607/) (2022)
- [Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering](https://mlsanity.com/paper/2309.06358/) (2023)
- [When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation](https://mlsanity.com/paper/2203.09391/) (2022)
- [Data Augmentation for Biomedical Factoid Question Answering](https://mlsanity.com/paper/2204.04711/) (2022)
- [QAGAN: Adversarial Approach To Learning Domain Invariant Language Features](https://mlsanity.com/paper/2206.12388/) (2022)
- [ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System](https://mlsanity.com/paper/2205.14981/) (2022)
- [Controllable Data Augmentation for Context-Dependent Text-to-SQL](https://mlsanity.com/paper/2304.13902/) (2023)
- [QOG:Question and Options Generation based on Language Model](https://mlsanity.com/paper/2406.12381/) (2024)
- [UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts](https://mlsanity.com/paper/2304.04539/) (2023)

## 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.