Fetching the paper…
Reading the bibliography…
Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis.
Maximum entropy models for natural language ambiguity resolution
A. Ratnaparkhi · 1998
Earlier work this paper cites.
Identifying mislabeled training data
C. E. Brodley and M. A. Friedl · 1999
Earlier work this paper cites.
Text chunking using transformation-based learning
L. A. Ramshaw and M. P. Marcus · 1999
Earlier work this paper cites.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
E. F. Tjong Kim Sang and F. De Meulder · 2003
Earlier work this paper cites.
Learning with noisy labels revisited: A study using real-world human annotations
J. Wei, Z. Zhu, H. Cheng, T. Liu, G. Niu, and Y. Liu · 2003
Earlier work this paper cites.
The relationship between precision-recall and ROC curves
J. Davis and M. Goadrich · 2006
Earlier work this paper cites.
Learning with noisy labels
N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari · 2013
Earlier work this paper cites.
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
T. Saito and M. Rehmsmeier · 2015
Cited alongside, same era.
Understanding and utilizing deep neural networks trained with noisy labels
P. Chen, B. B. Liao, G. Chen, and S. Zhang · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
Identifying mislabeled instances in classification datasets
N. M. Müller and K. Markert · 2019
Cited alongside, same era.
Crossweigh: Training named entity tagger from imperfect annotations
Z. Wang, J. Shang, L. Liu, L. Lu, J. Liu, and J. Han · 2019
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Beyond synthetic noise: Deep learning on controlled noisy labels
L. Jiang, D. Huang, M. Liu, and W. Yang · 2020
Later among the works it cites.
Identifying incorrect labels in the CoNLL-2003 corpus
F. Reiss, H. Xu, B. Cutler, K. Muthuraman, and Z. Eichenberger · 2020
Later among the works it cites.
An instance-dependent simulation framework for learning with label noise
K. Gu, X. Masotto, V. Bachani, B. Lakshminarayanan, J. Nikodem, and D. Yin · 2021
Later among the works it cites.
Annotation error detection: Analyzing the past and present for a more coherent future
J.-C. Klie, B. Webber, and I. Gurevych · 2022
Closest in time.
Model-agnostic label quality scoring to detect real-world label errors
J. Kuan and J. Mueller · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov · 2020
Cited alongside, same era.
bert-base-ner
L. David S
Cited in the paper.
Pervasive label errors in test sets destabilize machine learning benchmarks
C. G. Northcutt, A. Athalye, and J. Mueller
Cited in the paper.
Confident learning: Estimating uncertainty in dataset labels
C. G. Northcutt, L. Jiang, and I. L. Chuang
Cited in the paper.
xlm-roberta-large-finetuned-conll03-english
The team at Hugging Face
Cited in the paper.
Learning from noisy labels with deep neural networks: A survey
H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee · 2022
Closest in time.