CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection

Han, X., Eisenstein, J.: Unsupervised domain adaptation of contextualized embeddings: A case study in early modern english. CoRR (2019), http://arxiv.org/abs/1904.02817

Original

1904

Earlier work this paper cites.

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: A robustly optimized BERT pretraining approach. CoRR (2019), http://arxiv.org/abs/1907.11692

Original

1907

Earlier work this paper cites.

Xu, J., Xiao, L., López, A.M.: Self-supervised domain adaptation for computer vision tasks. CoRR (2019), http://arxiv.org/abs/1907.10915

Original

1907

Earlier work this paper cites.

Martin, L., Müller, B., Suárez, P.J.O., Dupont, Y., Romary, L., de la Clergerie, É.V., Seddah, D., Sagot, B.: CamemBERT: A tasty french language model. CoRR (2019), http://arxiv.org/abs/1911.03894

Original

1911

Earlier work this paper cites.

de Vries, W., van Cranenburgh, A., Bisazza, A., Caselli, T., van Noord, G., Nissim, M.: Bertje: A Dutch BERT model. CoRR (2019), http://arxiv.org/abs/1912.09582

Original

1912

Earlier work this paper cites.

McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12

1947

Earlier work this paper cites.

Müller, M., Salathé, M., Kummervold, P.E.: COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter. CoRR (2020), https://arxiv.org/abs/2005.07503

Original

2005

Earlier work this paper cites.

Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Rangel Pardo, F.M., Rosso, P., Sanguinetti, M.: SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation. pp. 54–63. Association for Computational Linguistics, Minneapolis (MN), USA (2019). https://doi.org/10.18653/v1/S19-2007

2007

Earlier work this paper cites.

Farahani, A., Voghoei, S., Rasheed, K., Arabnia, H.R.: A brief review of domain adaptation. CoRR (2020), https://arxiv.org/abs/2010.03978

Original

2010

Cited alongside, same era.

De Smedt, T., Daelemans, W.: Pattern for Python. J. Mach. Learn. Res. 13

2012

Cited alongside, same era.

Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext.zip: Compressing text classification models. CoRR (2016), http://arxiv.org/abs/1612.03651

Original

2016

Cited alongside, same era.

Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. Journal of Big data 3

2016

Cited alongside, same era.

Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. CoRR (2018), http://arxiv.org/abs/1810.04805

Original

2018

Kreutz, T., Daelemans, W.: How to optimize your Twitter collection. Computational Linguistics in the Netherlands Journal 9

2019

Later among the works it cites.

Delobelle, P., Winters, T., Berendt, B.: RobBERT: a Dutch RoBERTa-based Language Model. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 3255–3265. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.292

2020

Later among the works it cites.

Leong, C.W.B., Beigman Klebanov, B., Hamill, C., Stemle, E., Ubale, R., Chen, X.: A report on the 2020 VUA and TOEFL metaphor detection shared task. In: Proceedings of the Second Workshop on Figurative Language Processing. pp. 18–29. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.figlang-1.3

2020

Later among the works it cites.

Wang, S., Schraagen, M., Tjong Kim Sang, E., Dastani, M.: Public sentiment on governmental COVID-19 measures in Dutch social media. In: Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020. Association for Computational Linguistics, Online (Dec 2020). https://doi.org/10.18653/v1/2020.nlpcovid19-2.17