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Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates.
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 · 1902
Earlier work this paper cites.
“Cloze procedure”: A new tool for measuring readability
Wilson L Taylor. 1953 · 1953
Earlier work this paper cites.
Overview of the trec 2001 question answering track
Ellen M Voorhees. 2001 · 2001
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003 · 2003
Earlier work this paper cites.
The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al. 2009 · 2009
Earlier work this paper cites.
Driving semantic parsing from the world’s response
James Clarke, Dan Goldwasser, Ming-Wei Chang, and Dan Roth. 2010 · 2010
Earlier work this paper cites.
Entity based q&a retrieval
Amit Singh. 2012 · 2012
Earlier work this paper cites.
Weakly supervised learning of semantic parsers for mapping instructions to actions
Yoav Artzi and Luke Zettlemoyer. 2013 · 2013
Earlier work this paper cites.
Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
Earlier work this paper cites.
Scaling semantic parsers with on-the-fly ontology matching
Tom Kwiatkowski, Eunsol Choi, Yoav Artzi, and Luke Zettlemoyer. 2013 · 2013
Earlier work this paper cites.
Learning dependency-based compositional semantics
Percy Liang, Michael I Jordan, and Dan Klein. 2013 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Cited alongside, same era.
Open question answering over curated and extracted knowledge bases
Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2014 · 2014
Cited alongside, same era.
Modeling of the question answering task in the yodaqa system
Petr Baudis and Jan Sedivý. 2015 · 2015
Cited alongside, same era.
Learning recurrent span representations for extractive question answering
Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, and Jonathan Berant. 2016 · 2016
Cited alongside, same era.
Improving information extraction by acquiring external evidence with reinforcement learning
Karthik Narasimhan, Adam Yala, and Regina Barzilay. 2016 · 2016
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Adaptive document retrieval for deep question answering
Bernhard Kratzwald and Stefan Feuerriegel. 2018 · 2018
Later among the works it cites.
Ranking paragraphs for improving answer recall in open-domain question answering
Jinhyuk Lee, Seongjun Yun, Hyunjae Kim, Miyoung Ko, and Jaewoo Kang. 2018 · 2018
Later among the works it cites.
An efficient framework for learning sentence representations
Lajanugen Logeswaran and Honglak Lee. 2018 · 2018
Later among the works it cites.
Evaluation of sentence embeddings in downstream and linguistic probing tasks
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Cited alongside, same era.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Quasar: Datasets for question answering by search and reading
Bhuwan Dhingra, Kathryn Mazaitis, and William W Cohen. 2017 · 2017
Cited alongside, same era.
Searchqa: A new q&a dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
Cited alongside, same era.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Anserini: Enabling the use of lucene for information retrieval research
Peilin Yang, Hui Fang, and Jimmy Lin. 2017 · 2017
Cited alongside, same era.
Christian S Perone, Roberto Silveira, and Thomas S Paula. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
R 3: Reinforced ranker-reader for open-domain question answering
Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerry Tesauro, Bowen Zhou, and Jing Jiang. 2018 · 2018
Later among the works it cites.
Multi-step retriever-reader interaction for scalable open-domain question answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. 2019 · 2019
Closest in time.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Rhinehart, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, et al. 2019 · 2019
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The neural hype and comparisons against weak baselines
Jimmy Lin. 2019 · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Closest in time.