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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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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