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Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training.
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Continuous dice coefficient: a method for evaluating probabilistic segmentations
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Xlnet: Generalized autoregressive pretraining for language understanding
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Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
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A unified MRC framework for named entity recognition
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Semi-supervised sequence modeling with cross-view training
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Smote: Synthetic minority over-sampling technique
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder. 2003 · 2003
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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The penn chinese treebank: Phrase structure annotation of a large corpus
Naiwen Xue, Fei Xia, Fudong Choiu, and Marta Palmer. 2005 · 2005
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The third international Chinese language processing bakeoff: Word segmentation and named entity recognition
Gina-Anne Levow. 2006 · 2006
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A survey of named entity recognition and classification
David Nadeau and Satoshi Sekine. 2007 · 2007
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Ramoboost: Ranked minority oversampling in boosting
Shijuan Chen, Haibo He, and Edwardo A. Garcia. 2010 · 2010
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Self-paced learning for latent variable models
M. Pawan Kumar, Benjamin Packer, and Daphne Koller. 2010 · 2010
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Ensemble of exemplar-svms for object detection and beyond
Tomasz Malisiewicz, Abhinav Gupta, and Alexei A. Efros. 2011 · 2011
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Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task . ACL
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Named entity recognition in tweets: An experimental study
Alan Ritter, Sam Clark, Mausam, and Oren Etzioni. 2011 · 2011
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Towards robust linguistic analysis using OntoNotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2013 · 2014
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Fast r-cnn
Ross B. Girshick. 2015 · 2015
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A convolutional neural network cascade for face detection
Character-based joint segmentation and pos tagging for chinese using bidirectional rnn-crf
Yan Shao, Christian Hardmeier, Jörg Tiedemann, and Joakim Nivre. 2017 · 2017
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Reasonet: Learning to stop reading in machine comprehension
Yelong Shen, Po-Sen Huang, Jianfeng Gao, and Weizhu Chen. 2017 · 2017
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Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations
Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sébastien Ourselin, and M. Jorge Cardoso. 2017 · 2017
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Improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach
Sergi Valverde, Mariano Cabezas, Eloy Roura, Sandra González-Villà, Deborah Pareto, Joan C Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, Arnau Oliver, and Xavier Lladó. 2017 · 2017
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Morphosyntactic tagging with a meta-bilstm model over context sensitive token encodings
Bernd Bohnet, Ryan T. McDonald, Gonçalo Simões, Daniel Andor, Emily Pitler, and Joshua Maynez. 2018 · 2018
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H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua. 2015 · 2015
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Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. 2015 · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015 · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard Hovy. 2016 · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016 · 2016
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Ms marco: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Yang Fan, Fei Tian, Tao Qin, Xiuping Li, and Tie-Yan Liu. 2018 · 2018
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adboost: Thermal aware performance boosting through dark silicon patterning
Anil Kanduri, Mohammad Hashem Haghbayan, Amir M. Rahmani, Muhammad Shafique, Axel Jantsch, and Pasi Liljeberg. 2018 · 2018
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Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret. 2018 · 2018
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The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gáabor Melis, and Edward Grefenstette. 2018 · 2018
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Segmentation of head and neck organs at risk using CNN with batch dice loss
Oldrich Kodym, Michal Spanel, and Adam Herout. 2018 · 2018
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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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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Chen Shen, Holger R. Roth, Hirohisa Oda, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, and Kensaku Mori. 2018 · 2018
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Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018b · 2018
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Chinese ner using lattice lstm
Yue Zhang and Jie Yang. 2018 · 2018
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Towards accurate one-stage object detection with ap-loss
Kean Chen, Jianguo Li, Weiyao Lin, John See, Ji Wang, Lingyu Duan, Zhibo Chen, Changwei He, and Junni Zou. 2019 · 2019
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Improving and Interpreting Neural Networks for Word-Level Prediction Tasks in Natural Language Processing
Fréderic Godin. 2019 · 2019
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Libra R-CNN: towards balanced learning for object detection
Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, and Dahua Lin. 2019 · 2019
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