Fetching the paper…
Reading the bibliography…
Crowdsourcing platforms are often used to collect datasets for training machine learning models, despite higher levels of inaccurate labeling compared to expert labeling.
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.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
Earlier work this paper cites.
Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and P. S. Sastry. 2017 · 1925
Earlier work this paper cites.
Maximum likelihood estimation of observer error-rates using the EM algorithm
Alexander Philip Dawid and Allan M Skene. 1979 · 1979
Earlier work this paper cites.
Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller. 2001 · 2001
Earlier work this paper cites.
Learning syntactic patterns for automatic hypernym discovery
Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. 2004 · 2004
Earlier work this paper cites.
An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven. 2008 · 2008
Earlier work this paper cites.
Get another label? improving data quality and data mining using multiple, noisy labelers
Victor S. Sheng, Foster J. Provost, and Panagiotis G. Ipeirotis. 2008 · 2008
Earlier work this paper cites.
Cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks
Rion Snow, Brendan O’Connor, Daniel Jurafsky, and Andrew Y. Ng. 2008 · 2008
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
Supervised learning from multiple experts: whom to trust when everyone lies a bit
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, and Linda Moy. 2009 · 2009
Earlier work this paper cites.
Self-paced learning for latent variable models
M. Pawan Kumar, Benjamin Packer, and Daphne Koller. 2010 · 2010
Earlier work this paper cites.
The multidimensional wisdom of crowds
Peter Welinder, Steve Branson, Serge J. Belongie, and Pietro Perona. 2010 · 2010
Earlier work this paper cites.
Closing the loop: Fast, interactive semi-supervised annotation with queries on features and instances
Burr Settles. 2011 · 2011
Earlier work this paper cites.
Learning to label aerial images from noisy data
Volodymyr Mnih and Geoffrey E. Hinton. 2012 · 2012
Earlier work this paper cites.
Learning whom to trust with MACE
Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, and Eduard H. Hovy. 2013 · 2013
Earlier work this paper cites.
Theory of disagreement-based active learning
Steve Hanneke. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Self-paced curriculum learning
Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander G. Hauptmann. 2015 · 2015
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich. 2015 · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015 · 2015
Earlier work this paper cites.
Training convolutional neural networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus. 2015 · 2015
Earlier work this paper cites.
Semantic annotation aggregation with conditional crowdsourcing models and word embeddings
Paul Felt, Eric K. Ringger, and Kevin D. Seppi. 2016 · 2016
Cited alongside, same era.
Achieving budget-optimality with adaptive schemes in crowdsourcing
Ashish Khetan and Sewoong Oh. 2016 · 2016
Cited alongside, same era.
Re-active learning: Active learning with relabeling
Christopher H. Lin, Mausam, and Daniel S. Weld. 2016 · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, and Simon Lacoste-Julien. 2017 · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven. 2017 · 2017
Cited alongside, same era.
Bag of tricks for efficient text classification
Practical obstacles to deploying active learning
David Lowell, Zachary C. Lipton, and Byron C. Wallace. 2019 · 2019
Later among the works it cites.
Inherent disagreements in human textual inferences
Ellie Pavlick and Tom Kwiatkowski. 2019 · 2019
Later among the works it cites.
ATOMIC: an atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi. 2019a · 2019
Later among the works it cites.
Social iqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019b · 2019
Later among the works it cites.
Analysis of automatic annotation suggestions for hard discourse-level tasks in expert domains
Claudia Schulz, Christian M. Meyer, Jan Kiesewetter, Michael Sailer, Elisabeth Bauer, Martin R. Fischer, Frank Fischer, and Iryna Gurevych. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomás Mikolov. 2017 · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. 2017 · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
Cited alongside, same era.
NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017 · 2017
Cited alongside, same era.
Who said what: Modeling individual labelers improves classification
Melody Y. Guan, Varun Gulshan, Andrew M. Dai, and Geoffrey E. Hinton. 2018 · 2018
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama. 2018 · 2018
Cited alongside, same era.
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, and Nathan Silberman. 2019 · 2019
Later among the works it cites.
Learning with noisy labels for sentence-level sentiment classification
Hao Wang, Bing Liu, Chaozhuo Li, Yan Yang, and Tianrui Li. 2019 · 2019
Later among the works it cites.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, and Masashi Sugiyama. 2019 · 2019
Later among the works it cites.
Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020 · 2020
Later among the works it cites.
Uncertain natural language inference
Tongfei Chen, Zhengping Jiang, Adam Poliak, Keisuke Sakaguchi, and Benjamin Van Durme. 2020 · 2020
Later among the works it cites.
GoEmotions: A Dataset of Fine-Grained Emotions
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi. 2020 · 2020
Later among the works it cites.
Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven C. H. Hoi. 2020 · 2020
Later among the works it cites.
Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, and Kilian Q. Weinberger. 2020 · 2020
Later among the works it cites.
Social bias frames: Reasoning about social and power implications of language
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Model-agnostic methods for text classification with inherent noise
Kshitij Tayal, Rahul Ghosh, and Vipin Kumar. 2020 · 2020
Later among the works it cites.
Pre-train or annotate? domain adaptation with a constrained budget
Fan Bai, Alan Ritter, and Wei Xu. 2021 · 2021
Closest in time.
DeBERTa: Decoding-enhanced BERT with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
Closest in time.
Dynasent: A dynamic benchmark for sentiment analysis
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela. 2021 · 2021
Closest in time.
Learning with different amounts of annotation: From zero to many labels
Shujian Zhang, Chengyue Gong, and Eunsol Choi. 2021 · 2021
Closest in time.
Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
Jacob Whitehill, Paul Ruvolo, Tingfan Wu, Jacob Bergsma, and Javier R. Movellan. 2009 · 2043
Closest in time.
Self-paced learning with diversity
Lu Jiang, Deyu Meng, Shoou-I Yu, Zhen-Zhong Lan, Shiguang Shan, and Alexander G. Hauptmann. 2014 · 2086
Closest in time.