Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
Zhen Huang,
Shiyi Xu,
Minghao Hu,
Xinyi Wang,
Jinyan Qiu,
Yongquan Fu,
Yuncai Zhao,
Yuxing Peng,
Changjian Wang
Affiliations
Zhen Huang
Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Minghao Hu
Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Xinyi Wang
Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Jinyan Qiu
H.R. Support Center, Beijing, China
Yongquan Fu
Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Yuncai Zhao
Unit 31011, People’s Liberation Army, Beijing, China
Yuxing Peng
Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Changjian Wang
Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, China
Open-domain textual question answering (QA), which aims to answer questions from large data sources like Wikipedia or the web, has gained wide attention in recent years. Recent advancements in open-domain textual QA are mainly due to the significant developments of deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval, which allows the models to continuously refresh state-of-the-art performances. However, a comprehensive review of existing approaches and recent trends is lacked in this field. To address this issue, we present a thorough survey to explicitly give the task scope of open-domain textual QA, overview recent key advancements on deep learning based open-domain textual QA, illustrate the models and acceleration methods in detail, and introduce open-domain textual QA datasets and evaluation metrics. Finally, we summary the models, discuss the limitations of existing works and potential future research directions.
