Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

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, especial...

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Main Authors: Zhen Huang, Shiyi Xu, Minghao Hu, Xinyi Wang, Jinyan Qiu, Yongquan Fu, Yuncai Zhao, Yuxing Peng, Changjian Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9072442/
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spelling doaj-09371752c15a407f8a6ff7cabf847c9a2021-03-30T03:01:41ZengIEEEIEEE Access2169-35362020-01-018943419435610.1109/ACCESS.2020.29889039072442Recent Trends in Deep Learning Based Open-Domain Textual Question Answering SystemsZhen Huang0Shiyi Xu1https://orcid.org/0000-0003-4158-1051Minghao Hu2Xinyi Wang3Jinyan Qiu4Yongquan Fu5Yuncai Zhao6Yuxing Peng7Changjian Wang8Science and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, ChinaScience and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, ChinaScience and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, ChinaScience and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, ChinaH.R. Support Center, Beijing, ChinaScience and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, ChinaUnit 31011, People’s Liberation Army, Beijing, ChinaScience and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, ChinaScience and Technology on Parallel and Distributed Laboratory, College of Computer Science and Technology, National University of Defense Technology, Changsha, ChinaOpen-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.https://ieeexplore.ieee.org/document/9072442/Open-domain textual question answeringdeep learningmachine reading comprehensioninformation retrieval
collection DOAJ
language English
format Article
sources DOAJ
author Zhen Huang
Shiyi Xu
Minghao Hu
Xinyi Wang
Jinyan Qiu
Yongquan Fu
Yuncai Zhao
Yuxing Peng
Changjian Wang
spellingShingle Zhen Huang
Shiyi Xu
Minghao Hu
Xinyi Wang
Jinyan Qiu
Yongquan Fu
Yuncai Zhao
Yuxing Peng
Changjian Wang
Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
IEEE Access
Open-domain textual question answering
deep learning
machine reading comprehension
information retrieval
author_facet Zhen Huang
Shiyi Xu
Minghao Hu
Xinyi Wang
Jinyan Qiu
Yongquan Fu
Yuncai Zhao
Yuxing Peng
Changjian Wang
author_sort Zhen Huang
title Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
title_short Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
title_full Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
title_fullStr Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
title_full_unstemmed Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems
title_sort recent trends in deep learning based open-domain textual question answering systems
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description 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.
topic Open-domain textual question answering
deep learning
machine reading comprehension
information retrieval
url https://ieeexplore.ieee.org/document/9072442/
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