论文标题
机器阅读理解系统的调查
A Survey on Machine Reading Comprehension Systems
论文作者
论文摘要
机器阅读理解是自然语言处理中的一项具有挑战性的任务和热门话题。它的目标是开发系统来回答有关给定环境的问题。在本文中，我们对机器阅读理解系统的不同方面进行了全面调查，包括其方法，结构，输入/输出和研究新颖性。我们根据2016年至2020年的241篇论文说明了该领域的最新趋势。我们的研究表明，近年来，研究重点从答案提取到答案，从单身到多文件阅读理解，从Scratch学习到从Scratch学习到使用预训练的嵌入。我们还讨论了该领域的流行数据集和评估指标。该论文以调查最引用的论文及其贡献结尾。
Machine reading comprehension is a challenging task and hot topic in natural language processing. Its goal is to develop systems to answer the questions regarding a given context. In this paper, we present a comprehensive survey on different aspects of machine reading comprehension systems, including their approaches, structures, input/outputs, and research novelties. We illustrate the recent trends in this field based on 241 reviewed papers from 2016 to 2020. Our investigations demonstrate that the focus of research has changed in recent years from answer extraction to answer generation, from single to multi-document reading comprehension, and from learning from scratch to using pre-trained embeddings. We also discuss the popular datasets and the evaluation metrics in this field. The paper ends with investigating the most cited papers and their contributions.
