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English Information

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Authors
# Name
1 Helena Caseli(e-mail do docente Helena de Medeiros Caseli)
2 Cláudia Freitas(claudiafreitas@puc-rio.br)
3 Roberta Viola(robertaviola@ufmg.com)

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Reference
# Reference
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2 AZIZ, W.; SPECIA, L. Fully automatic compilation of a Portuguese-English parallel corpus for statistical machine translation. In: STIL 2011. Cuiabá, MT: [s.n.], 2011.
3 BICK, E. The Parsing System “Palavras”. Automatic Grammatical Analysis of Portuguese in a Constraint Grammar Framework. Århus: University of Arhus, 2000.
4 BOJANOWSKI, P. et al. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016.
5 BROWN, T. et al. Language models are few-shot learners. In: LAROCHELLE, H. et al. (Ed.). Advances in Neural Information Processing Systems. Curran Associates, Inc., 2020. v. 33, p. 1877–1901.
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7 DEVLIN, J. et al. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota: Association for Computational Linguistics, 2019. p. 4171–4186.
8 JOSHI, M. et al. SpanBERT: Improving Pre-training by Representing and Predicting Spans. Transactions of the Association for Computational Linguistics, v. 8, p. 64–77, 01 2020. ISSN 2307-387X.
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11 LIU, Y. et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach. 2019. Cite arxiv:1907.11692
12 MCSHANE, M.; NIRENBURG, S. Linguistics for the Age of AI. The MIT Press, 2021. ISBN 9780262363136.
13 MIKOLOV, T. et al. Efficient estimation of word representations in vector space. Proceedings of Workshop at ICLR, 2013.
14 OLIVEIRA, H. G. et al. As wordnets do português. Oslo Studies in Language, v. 7, n. 1, p. 397–424, 2015.
15 PAPINENI, K. et al. Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, 2002. p. 311–318.
16 PENNINGTON, J.; SOCHER, R.; MANNING, C. GloVe: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics, 2014. p. 1532–1543.
17 Santos et al. 2010 Relações semânticas em português: comparando o TeP, o MWN.PT, o Port4NooJ e o PAPEL. [S.l.]: Associação Portuguesa de Linguística: Lisboa, 2010. 681–700 p.
18 SOUZA, F.; NOGUEIRA, R.; LOTUFO, R. BERTimbau: Pretrained BERT Models for Brazilian Portuguese. In: CERRI, R.; PRATI, R. C. (Ed.). Intelligent Systems. Cham: Springer International Publishing, 2020. p. 403–417. ISBN 978-3-030-61377-8.