# A Survey on Neural Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1906.03824 · HTML: https://mlsanity.com/paper/1906.03824/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [CodeQA: A Question Answering Dataset for Source Code Comprehension](https://mlsanity.com/paper/2109.08365/) (2021)
- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [MORABLES: A Benchmark for Assessing Abstract Moral Reasoning in LLMs with Fables](https://mlsanity.com/paper/2509.12371/) (2025)
- [PSG: Prompt-based Sequence Generation for Acronym Extraction](https://mlsanity.com/paper/2111.14301/) (2021)
- [Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model](https://mlsanity.com/paper/2107.01571/) (2021)
- [emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information](https://mlsanity.com/paper/2404.12050/) (2024)
- [Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets](https://mlsanity.com/paper/2408.02114/) (2024)

## Abstract (arXiv)

Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge. In recent years, the popularity of deep learning and the establishment of large-scale datasets have both promoted the prosperity of Machine Reading Comprehension. This paper aims to present how to utilize the Neural Network to build a Reader and introduce some classic models, analyze what improvements they make. Further, we also point out the defects of existing models and future research directions