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Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
A thorough examination of the CNN/Daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Earlier work this paper cites.
Coarse-to-fine question answering for long documents
Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, and Jonathan Berant. 2017 · 2017
Earlier work this paper cites.
Bi-directional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hananneh Hajishirzi. 2017 · 2017
Earlier work this paper cites.
NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Multi-step retriever-reader interaction for scalable open-domain question answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. 2018 · 2018
Cited alongside, same era.
A unified model for document-based question answering based on human-like reading strategy
Weikang Li, Wei Li, and Yunfang Wu. 2018 · 2018
Cited alongside, same era.
Multi-mention learning for reading comprehension with neural cascades
Swabha Swayamdipta, Ankur P Parikh, and Tom Kwiatkowski. 2018 · 2018
Cited alongside, same era.
Densely connected attention propagation for reading comprehension
Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and Jian Su. 2018 · 2018
Cited alongside, same era.
Evidence aggregation for answer re-ranking in open-domain question answering
Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, and Murray Campbell. 2018 · 2018
Cited alongside, same era.
Natural Questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
Later among the works it cites.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Later among the works it cites.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Later among the works it cites.
Learning to retrieve reasoning paths over Wikipedia graph for question answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020 · 2020
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
MRQA 2019 shared task: Evaluating generalization in reading comprehension
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019 · 2019
Cited alongside, same era.
A discrete hard EM approach for weakly supervised question answering
Sewon Min, Danqi Chen, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019a
Cited in the paper.
Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019b
Cited in the paper.
SpanBERT: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
Closest in time.
A self-training method for machine reading comprehension with soft evidence extraction
Yilin Niu, Fangkai Jiao, Mantong Zhou, Ting Yao, Jingfang Xu, and Minlie Huang. 2020 · 2020
Closest in time.