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As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE.
Language models are few-shot learners
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Appendix A: Evaluation task description
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Fourth Message Understanding Conference (MUC-4): Proceedings of a Conference Held in McLean, Virginia, June 16-18, 1992
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MUC-4 evaluation metrics
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Essays on Actions and Events: Philosophical Essays Volume 1
Donald Davidson. 2001 · 2001
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Nathanael Chambers and Dan Jurafsky. 2008 · 2008
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Unsupervised learning of narrative schemas and their participants
Nathanael Chambers and Dan Jurafsky. 2009 · 2009
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A unified model of phrasal and sentential evidence for information extraction
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Multi-sentence argument linking
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Event extraction as multi-turn question answering
Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, and Yong Zhu. 2020 · 2020
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Event extraction as machine reading comprehension
Jian Liu, Yubo Chen, Kang Liu, Wei Bi, and Xiaojiang Liu. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Template-based information extraction without the templates
Nathanael Chambers and Dan Jurafsky. 2011 · 2011
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Peeling back the layers: Detecting event role fillers in secondary contexts
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Modeling textual cohesion for event extraction
Ruihong Huang and Ellen Riloff. 2012 · 2012
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Events are not simple: Identity, non-identity, and quasi-identity
Eduard Hovy, Teruko Mitamura, Felisa Verdejo, Jun Araki, and Andrew Philpot. 2013 · 2013
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Content analysis: An introduction to its methodology
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Template filling with generative transformers
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Argument linking: A survey and forecast
William Gantt. 2021 · 2021
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Document-level entity-based extraction as template generation
Kung-Hsiang Huang, Sam Tang, and Nanyun Peng. 2021 · 2021
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Speech and Language Processing
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Document-level event argument extraction by conditional generation
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Automatic error analysis for document-level information extraction
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Asking the right questions in low resource template extraction
Nils Holzenberger, Yunmo Chen, and Benjamin Van Durme. 2022 · 2022
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Iterative document-level information extraction via imitation learning
Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen, Aaron White, and Benjamin Van Durme. 2023 · 2023
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The better cross-language datasets
Ian Soboroff. 2023 · 2023
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