# English Machine Reading Comprehension Datasets: A Survey

arXiv: https://arxiv.org/abs/2101.10421 · HTML: https://mlsanity.com/paper/2101.10421/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 84 of its 141 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 30 of 84)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference](https://mlsanity.com/paper/1808.05326/) (2018)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)

## Built on by (most cited first, 26 of 26)

- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections](https://mlsanity.com/paper/2109.03438/) (2021)
- [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2402.01512/) (2024)
- [INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages](https://mlsanity.com/paper/2407.13522/) (2024)
- [VASR: Visual Analogies of Situation Recognition](https://mlsanity.com/paper/2212.04542/) (2022)
- [Improving Unsupervised Question Answering via Summarization-Informed Question Generation](https://mlsanity.com/paper/2109.07954/) (2021)
- [MetaQA: Combining Expert Agents for Multi-Skill Question Answering](https://mlsanity.com/paper/2112.01922/) (2021)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [A Survey of Machine Narrative Reading Comprehension Assessments](https://mlsanity.com/paper/2205.00299/) (2022)
- [Clickbait Spoiling via Question Answering and Passage Retrieval](https://mlsanity.com/paper/2203.10282/) (2022)
- [More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering](https://mlsanity.com/paper/2109.12264/) (2021)
- [Desiderata for the Context Use of Question Answering Systems](https://mlsanity.com/paper/2401.18001/) (2024)
- [A Survey on non-English Question Answering Dataset](https://mlsanity.com/paper/2112.13634/) (2021)
- [ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text](https://mlsanity.com/paper/2303.18162/) (2023)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [It Is Not About What You Say, It Is About How You Say It: A Surprisingly Simple Approach for Improving Reading Comprehension](https://mlsanity.com/paper/2406.16779/) (2024)
- [BanglaQuAD: A Bengali Open-domain Question Answering Dataset](https://mlsanity.com/paper/2410.10229/) (2024)
- [Generating Answer Candidates for Quizzes and Answer-Aware Question Generators](https://mlsanity.com/paper/2108.12898/) (2021)
- [Literature review on vulnerability detection using NLP technology](https://mlsanity.com/paper/2104.11230/) (2021)
- [AmQA: Amharic Question Answering Dataset](https://mlsanity.com/paper/2303.03290/) (2023)
- [VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension](https://mlsanity.com/paper/2402.02655/) (2024)
- [Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models](https://mlsanity.com/paper/2406.13232/) (2024)
- [Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop](https://mlsanity.com/paper/2206.14272/) (2022)
- [Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering](https://mlsanity.com/paper/2303.12671/) (2023)
- [Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets](https://mlsanity.com/paper/2408.02114/) (2024)
- [Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2503.18062/) (2025)

## Abstract (arXiv)

This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem. We categorize the datasets according to their question and answer form and compare them across various dimensions including size, vocabulary, data source, method of creation, human performance level, and first question word. Our analysis reveals that Wikipedia is by far the most common data source and that there is a relative lack of why, when, and where questions across datasets.