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TACRED (Zhang et al., 2017) is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE).
Simple bert models for relation extraction and semantic role labeling
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Modeling Relations and Their Mentions without Labeled Text
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Relation classification via convolutional deep neural network
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A thorough examination of the CNN/daily mail reading comprehension task
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Question answering on freebase via relation extraction and textual evidence
Kun Xu, Siva Reddy, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2016 · 2016
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Position-aware attention and supervised data improve slot filling
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Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, and Max Welling. 2017 · 2017
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Probing what different NLP tasks teach machines about function word comprehension
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Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao. 2019 · 2019
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Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Graph convolution over pruned dependency trees improves relation extraction
Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018 · 2018
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Improving relation extraction by pre-trained language representations
Christoph Alt, Marc Hübner, and Leonhard Hennig. 2019 · 2019
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Matching the blanks: Distributional similarity for relation learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019 · 2019
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Knowledge enhanced contextual word representations
Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019 · 2019
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Errudite: Scalable, reproducible, and testable error analysis
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel Weld. 2019 · 2019
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ERNIE: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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