A Survey on Neural Machine Reading Comprehension

Abstract

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

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Cited by in corpus (10)

• QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
• Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
• A Survey on Explainability in Machine Reading Comprehension
• Neural Machine Reading Comprehension: Methods and Trends
• A Survey of Document Grounded Dialogue Systems (DGDS)
• A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
• PSG: Prompt-based Sequence Generation for Acronym Extraction
• Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources
• Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model
• CodeQA: A Question Answering Dataset for Source Code Comprehension