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BERT (Bidirectional Encoder Representations from Transformers) and ALBERT (A Lite BERT) are methods for pre-training language models which can later be fine-tuned for a variety of Natural Language Understanding tasks.
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
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
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Using linguistic information to classify portuguese text documents
Teresa Gonçalves and Paulo Quaresma. 2008 · 2008
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Identifying emotions in short texts for brazilian portuguese
Barbara Martinazzo, Mariza Miola Dosciatti, and Emerson Cabrera Paraiso. 2011 · 2011
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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An evaluation of machine translation for multilingual sentence-level sentiment analysis
Matheus Araujo, Julio Reis, Adriano Pereira, and Fabricio Benevenuto. 2016 · 2016
Earlier work this paper cites.
Inesc-id@assin: Medição de similaridade semântica e reconhecimento de inferência textual
Pedro Fialho, Ricardo Marques, Bruno Martins, Luísa Coheur, and Paulo Quaresma. 2016 · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Cited alongside, same era.
Multilingual emotion classification using supervised learning
Karin Becker, Viviane P. Moreira, and Aline G.L. dos Santos. 2017 · 2017
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Offensive comments in the brazilian web: a dataset and baseline results
SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson. 2018 · 2018
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Contributions to the study of fake news in portuguese: New corpus and automatic detection results
Rafael A. Monteiro, Roney L. S. Santos, Thiago A. S. Pardo, Tiago A. de Almeida, Evandro E. S. Ruiz, and Oto A. Vale. 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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Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Recognizing textual entailment: challenges in the portuguese language
Gil Rocha and Henrique L. Cardoso. 2018 · 2018
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Rogers P. de Pelle and Viviane P. Moreira. 2017 · 2017
Cited alongside, same era.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Telmo Pires, Eva Schlinger, and Dan Garrette. 2019 · 2019
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