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We cast a suite of information extraction tasks into a text-to-triple translation framework.
Language models are few-shot learners
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Roberta: A robustly optimized bert pretraining approach
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Efficient long-distance relation extraction with dg-spanbert
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Openie6: Iterative grid labeling and coordination analysis for open information extraction
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Language models are open knowledge graphs
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Identifying relations for open information extraction
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Open language learning for information extraction
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Effectiveness and efficiency of open relation extraction
Filipe Mesquita, Jordan Schmidek, and Denilson Barbosa. 2013 · 2013
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Open information extraction with tree kernels
Ying Xu, Mi-Young Kim, Kevin M Quinn, Randy Goebel, and Denilson Barbosa. 2013 · 2013
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Leveraging linguistic structure for open domain information extraction
Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D Manning. 2015 · 2015
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Question-answer driven semantic role labeling: Using natural language to annotate natural language
Luheng He, Mike Lewis, and Luke Zettlemoyer. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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Creating a large benchmark for open information extraction
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Getting more out of syntax with props
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Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
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Improving language understanding by generative pre-training
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Supervised open information extraction
Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, and Ido Dagan. 2018 · 2018
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Carb: A crowdsourced benchmark for open ie
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Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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How context affects language models’ factual predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Multiˆ2OIE: Multilingual open information extraction based on multi-head attention with BERT
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Language models as knowledge bases?
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Language models are unsupervised multitask learners
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Matching the blanks: Distributional similarity for relation learning
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Eliciting knowledge from language models using automatically generated prompts
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