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Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency.
Entity-relation extraction as multi-turn question answering
Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019 · 1905
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Unifiedqa: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
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Event extraction by answering (almost) natural questions
Xinya Du and Claire Cardie. 2020b · 2004
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
Earlier work this paper cites.
ACE 2005 multilingual training corpus (LDC2006T06)
Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006 · 2005
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End-to-end neural coreference resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018 · 2018
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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019 · 2019
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Deeper text understanding for IR with contextual neural language modeling
Zhuyun Dai and Jamie Callan. 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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Dialog state tracking: A neural reading comprehension approach
Shuyang Gao, Abhishek Sethi, Sanchit Agarwal, Tagyoung Chung, and Dilek Hakkani-Tur. 2019 · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
NLProlog: Reasoning with weak unification for question answering in natural language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel. 2019 · 2019
Cited alongside, same era.
Hierarchical entity typing via multi-level learning to rank
Tongfei Chen, Yunmo Chen, and Benjamin Van Durme. 2020a · 2020
Cited alongside, same era.
Reading the manual: Event extraction as definition comprehension
Yunmo Chen, Tongfei Chen, Seth Ebner, Aaron Steven White, and Benjamin Van Durme. 2020b · 2020
Cited alongside, same era.
Corefqa: Coreference resolution as query-based span prediction
Wei Wu, Fei Wang, Arianna Yuan, Fei Wu, and Jiwei Li. 2020 · 2020
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GRIT: generative role-filler transformers for document-level event entity extraction
Xinya Du, Alexander M. Rush, and Claire Cardie. 2021a · 2021
Later among the works it cites.
Template filling with generative transformers
Xinya Du, Alexander M. Rush, and Claire Cardie. 2021b · 2021
Later among the works it cites.
How many data points is a prompt worth?
Teven Le Scao and Alexander Rush. 2021 · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
Later among the works it cites.
Cutting down on prompts and parameters: Simple few-shot learning with language models
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Document-level event role filler extraction using multi-granularity contextualized encoding
Xinya Du and Claire Cardie. 2020a · 2020
Cited alongside, same era.
Multi-sentence argument linking
Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, and Benjamin Van Durme. 2020 · 2020
Cited alongside, same era.
A unified MRC framework for named entity recognition
Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
Joint modeling of arguments for event understanding
Yunmo Chen, Tongfei Chen, and Benjamin Van Durme. 2020c
Cited in the paper.
Robert L. Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2021 · 2021
Later among the works it cites.
Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
Later among the works it cites.
Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021a · 2021
Later among the works it cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021b · 2021
Later among the works it cites.
Large-scale QA-SRL parsing
Nicholas FitzGerald, Julian Michael, Luheng He, and Luke Zettlemoyer. 2018 · 2060
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