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In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets.
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Reading wikipedia to answer open-domain questions
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Searchqa: A new q&a dataset augmented with context from a search engine
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Coqa: A conversational question answering challenge
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End-to-end open-domain question answering with bertserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019 · 2019
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A survey on machine reading comprehension systems
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YAKE! Keyword extraction from single documents using multiple local features
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Yingqi Qu Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Wenpeng Hu, Bing Liu, Jinwen Ma, Dongyan Zhao, and Rui Yan. 2018 · 2018
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The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Self-training for jointly learning to ask and answer questions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . 629–640
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Yasunobu Sumikawa and Adam Jatowt. 2018 · 2018
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Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, and Shi Wang. 2018 · 2018
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Learning to ask questions in open-domain conversational systems with typed decoders
Yansen Wang, Chenyi Liu, Minlie Huang, and Liqiang Nie. 2018 · 2018
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Record: Bridging the gap between human and machine commonsense reading comprehension
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Genaug: Data augmentation for finetuning text generators
Steven Y Feng, Varun Gangal, Dongyeop Kang, Teruko Mitamura, and Eduard Hovy. 2020 · 2020
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Ghader Kurdi, Jared Leo, Bijan Parsia, Uli Sattler, and Salam Al-Emari. 2020 · 2020
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Yu Li, Xiao Li, Yating Yang, and Rui Dong. 2020 · 2020
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Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 2020
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Synthea™ Novel coronavirus (COVID-19) model and synthetic data set
Jason Walonoski, Sybil Klaus, Eldesia Granger, Dylan Hall, Andrew Gregorowicz, George Neyarapally, Abigail Watson, and Jeff Eastman. 2020 · 2020
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Answering event-related questions over long-term news article archives. In European conference on information retrieval . Springer, 774–789
Jiexin Wang, Adam Jatowt, Michael Färber, and Masatoshi Yoshikawa. 2020 · 2020
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A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets
Changchang Zeng, Shaobo Li, Qin Li, Jie Hu, and Jianjun Hu. 2020 · 2020
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English Machine Reading Comprehension Datasets: A Survey
Daria Dzendzik, Carl Vogel, and Jennifer Foster. 2021 · 2021
Closest in time.
Quiz-Style Question Generation for News Stories
Adam D Lelkes, Vinh Q Tran, and Cong Yu. 2021 · 2021
Closest in time.
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Anna Rogers, Matt Gardner, and Isabelle Augenstein. 2021 · 2021
Closest in time.
Question Answering Over Temporal Knowledge Graphs
Apoorv Saxena, Soumen Chakrabarti, and Partha Talukdar. 2021 · 2021
Closest in time.
Improving question answering for event-focused questions in temporal collections of news articles
Jiexin Wang, Adam Jatowt, Michael Färber, and Masatoshi Yoshikawa. 2021 · 2021
Closest in time.
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua. 2021 · 2021
Closest in time.