在科技发达和信息爆炸的时代, 如何从海量数据中准确地提取所需信息已成为人们研究的目标. 问答系统作为解决此问题的重要途径之一, 其主要通过对已有数据信息进行检索和分析, 并最终返回问题答案或其他相关信息. 近年来, 深度学习的革命性发展给问答系统带来了长足的进步, 序列到序列的模型, 端到端的模型以及最近流行的预训练, 都给问答系统留下无限的发展空间, 但其仍面临许多挑战. 本文首先对问答系统的发展进行简要介绍, 接着将问答系统按照3个不同角度进行分类, 并对相关数据集、评测指标和各类问答系统的主流技术进行阐述, 最后对问答系统面临的问题和未来的发展趋势进行讨论.
In the era featuring advanced technology and information explosion, how to accurately extract the required information from massive data has become the study target. As one of the important ways to solve this problem, question-answering systems mainly retrieve and analyze existing data and information and finally return the answer to the question or other related information. In recent years, the revolutionary development of deep learning has brought considerable progress to question-answering systems. Sequence-to-sequence models, end-to-end models, and the recently popular pre-training have left unlimited development space for the question-answering systems, but these systems still face many challenges. This study first briefly introduces the development of the question-answering systems, then classifies these systems from three different perspectives, and expounds on the relevant data sets, evaluation indicators, and mainstream technologies of various question-answering systems. Finally, the study discusses the problems faced by question-answering systems and their future development trends.