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Document-level Relation Extraction (DocRE), which aims to extract relations from a long context, is a critical challenge in achieving fine-grained structural comprehension and generating interpretable document representations.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
DREEAM: Guiding attention with evidence for improving document-level relation extraction
Youmi Ma, An Wang, and Naoaki Okazaki. 2023 · 1983
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Natural language inference
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Earlier work this paper cites.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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DocRED: A large-scale document-level relation extraction dataset
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. 2019 · 2019
Earlier work this paper cites.
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Earlier work this paper cites.
Reasoning with latent structure refinement for document-level relation extraction
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Earlier work this paper cites.
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Earlier work this paper cites.
Global-to-local neural networks for document-level relation extraction
Difeng Wang, Wei Hu, Ermei Cao, and Weijian Sun. 2020 · 2020
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Document-level relation extraction as semantic segmentation
Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, and Huajun Chen. 2021 · 2021
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Document-level relation extraction with adaptive thresholding and localized context pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma, and Jing Huang. 2021 · 2021
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Structured information extraction from complex scientific text with fine-tuned large language models
Alexander Dunn, John Dagdelen, Nicholas Walker, Sanghoon Lee, Andrew S Rosen, Gerbrand Ceder, Kristin Persson, and Anubhav Jain. 2022 · 2022
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Revisiting relation extraction in the era of large language models
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Revisiting docred – addressing the false negative problem in relation extraction
Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, and Sharifah Mahani Aljunied. 2022b
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