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Many question answering (QA) tasks only provide weak supervision for how the answer should be computed.
A BERT baseline for the Natural Questions
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
Luke S. Zettlemoyer and Michael Collins. 2005 · 2005
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Comparing measures of sparsity
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The probabilistic relevance framework: BM25 and beyond
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Multi-instance learning by treating instances as non-i.i.d. samples
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Driving semantic parsing from the world’s response
James Clarke, Dan Goldwasser, Ming-Wei Chang, and Dan Roth. 2010 · 2010
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Weakly supervised learning of semantic parsers for mapping instructions to actions
Yoav Artzi and Luke Zettlemoyer. 2013 · 2013
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Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Learning dependency-based compositional semantics
Percy Liang, Michael I Jordan, and Dan Klein. 2013 · 2013
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Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017 · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
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Neural semantic parsing with type constraints for semi-structured tables
Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner. 2017 · 2017
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Neural symbolic machines: Learning semantic parsers on Freebase with weak supervision
Multi-mention learning for reading comprehension with neural cascades
Swabha Swayamdipta, Ankur P Parikh, and Tom Kwiatkowski. 2018 · 2018
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Densely connected attention propagation for reading comprehension
Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and Jian Su. 2018 · 2018
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Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering
Wei Wang, Ming Yan, and Chen Wu. 2018 · 2018
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DCN+: Mixed objective and deep residual coattention for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2018 · 2018
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SQLNet: Generating structured queries from natural language without reinforcement learning
Xiaojun Xu, Chang Liu, and Dawn Song. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
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Chen Liang, Jonathan Berant, Quoc Le, Kenneth D Forbus, and Ni Lao. 2017 · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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Seq2SQL: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Neural program synthesis with priority queue training
Daniel A Abolafia, Mohammad Norouzi, Jonathan Shen, Rui Zhao, and Quoc V Le. 2018 · 2018
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Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner. 2018 · 2018
Cited alongside, same era.
Coarse-to-fine decoding for neural semantic parsing
Li Dong and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Learning to generalize from sparse and underspecified rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, and Mohammad Norouzi. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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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, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Change, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
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Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019 · 2019
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Multi-style generative reading comprehension
Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, and Junji Tomita. 2019 · 2019
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MultiQA: An empirical investigation of generalization and transfer in reading comprehension
Alon Talmor and Jonathan Berant. 2019 · 2019
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