Fetching the paper…
Reading the bibliography…
The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countries.
Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 1910
Earlier work this paper cites.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder · 2003
Earlier work this paper cites.
Design challenges and misconceptions in named entity recognition
Lev Ratinov and Dan Roth · 2009
Earlier work this paper cites.
Classification in the presence of label noise: A survey
B. Frenay and M. Verleysen · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
Earlier work this paper cites.
Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge J. Belongie · 2015
Earlier work this paper cites.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Earlier work this paper cites.
Training deep neural-networks based on unreliable labels
Alan Joseph Bekker and Jacob Goldberger · 2016
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2016
Earlier work this paper cites.
Learning when to trust distant supervision: An application to low-resource pos tagging using cross-lingual projection
Meng Fang and Trevor Cohn · 2016
Earlier work this paper cites.
Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
Earlier work this paper cites.
Restoring tone-marks in standard yoruba electronic text: Improved model
F. O. Asahiah, O. A. Odejobi, and E. R. Adagunodo · 2017
Earlier work this paper cites.
Language independent named entity recognition using distant supervision
Julia Dembowski, Michael Wiegand, and Dietrich Klakow · 2017
Earlier work this paper cites.
Webvision database: Visual learning and understanding from web data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
Earlier work this paper cites.
Cross-lingual name tagging and linking for 282 languages
Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji · 2017
Cited alongside, same era.
Detecting annotation noise in automatically labelled data
Ines Rehbein and Josef Ruppenhofer · 2017
Cited alongside, same era.
Contextual string embeddings for sequence labeling
Alan Akbik, Duncan Blythe, and Roland Vollgraf · 2018
Cited alongside, same era.
Learning how to self-learn: Enhancing self-training using neural reinforcement learning
Chenhua Chen, Yue Zhang, and Yuze Gao · 2018
Cited alongside, same era.
Overview of the DARPA LORELEI program
Caitlin Christianson, Jason Duncan, and Boyan A. Onyshkevych · 2018
Cited alongside, same era.
Training a neural network in a low-resource setting on automatically annotated noisy data
Michael A. Hedderich and Dietrich Klakow · 2018
Cloze-driven pretraining of self-attention networks
Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, and Michael Auli · 2019
Later among the works it cites.
Low-resource name tagging learned with weakly labeled data
Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, and Heng Ji · 2019
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Later among the works it cites.
Ethnologue: Languages of the world. twenty-second edition., 2019
David M. Eberhard, Gary F. Simons, and Charles D. Fennig (eds.) · 2019
Later among the works it cites.
Feature-dependent confusion matrices for low-resource ner labeling with noisy labels
Lukas Lange, Michael A. Hedderich, and Dietrich Klakow · 2019
Later among the works it cites.
Named entity recognition with partially annotated training data
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
Cited alongside, same era.
Talen: Tool for annotation of low-resource entities
Stephen Mayhew and Dan Roth · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Cited alongside, same era.
Domain-sensitive temporal tagging
Jannik Strötgen, Michael Gertz, Graeme Hirst, and Ruihong Huang · 2018
Cited alongside, same era.
Distantly supervised ner with partial annotation learning and reinforcement learning
Yaosheng Yang, Wenliang Chen, Zhenghua Li, Zhengqiu He, and Min Zhang · 2018
Cited alongside, same era.
Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai, and Dan Roth · 2019
Later among the works it cites.
Handling noisy labels for robustly learning from self-training data for low-resource sequence labeling
Debjit Paul, Mittul Singh, Michael A. Hedderich, and Dietrich Klakow · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Later among the works it cites.
Massively multilingual transfer for NER
Afshin Rahimi, Yuan Li, and Trevor Cohn · 2019
Later among the works it cites.
Snorkel: rapid training data creation with weak supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 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
Later among the works it cites.
Massive vs. curated word embeddings for low-resourced languages. the case of Yor \ \backslash ub \ \backslash ’a and Twi
Jesujoba O Alabi, Kwabena Amponsah-Kaakyire, David I Adelani, and Cristina España-Bonet · 2020
Closest in time.
Self: learning to filter noisy labels with self-ensembling
T. Nguyen, C. K. Mummadi, T. P. N. Ngo, T. H. P. Nguyen, L. Beggel, and T. Brox · 2020
Closest in time.
Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss
Barbara Plank, Anders Søgaard, and Yoav Goldberg · 2067
Closest in time.