SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Matheus Sanches(m212142@dac.unicamp.br)
2 Jader Martins(j234830@dac.unicamp.br)
3 Henrique Foerste(h236651@dac.unicamp.br)
4 Rafael Souza(rroque@unicamp)
5 Julio Dos Reis(jreis@ic.unicamp.br)
6 Leandro Villas(lvillas@unicamp.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 [Baroni et al. 2009] Baroni, M., Bernardini, S., Ferraresi, A., and Zanchetta, E. (2009). The WaCky wide web: a collection of very large linguistically processed web-crawled cor- pora. Language Resources and Evaluation, 43(3):209–226.
2 [Budzianowski et al. 2018] Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., and Gaˇsi ́c, M. (2018). Multiwoz – a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling.
3 [Devlin et al. 2018] Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding.
4 [Gonc ̧alves 2022] Gonc ̧alves, L. (2022). Imdb pt-br. https://www.kaggle.com/ datasets/luisfredgs/imdb-ptbr. Accessed: 2022-05-25.
5 [Guillou 2020] Guillou, P. (2020). Gportuguese-2 (portuguese gpt-2 small): a language model for portuguese text generation (and more nlp tasks...).
6 [Howard and Ruder 2018] Howard, J. and Ruder, S. (2018). Universal language model fine-tuning for text classification.
7 [HuggingFace 2022a] HuggingFace (2022a). Hugging face – the ai community building the future. https://huggingface.co/datasets?languages=languages: en. Accessed: 2022-05-25.
8 [HuggingFace 2022b] HuggingFace (2022b). Hugging face – the ai community building the future. https://huggingface.co/datasets?languages=languages: pt. Accessed: 2022-05-25.
9 [Kaplan et al. 2020] Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020). Scaling laws for neural language models.
10 [Lowe et al. 2016] Lowe, R., Pow, N., Serban, I., and Pineau, J. (2016). The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems.
11 [Meta 2021] Meta (2021). Main page — meta, discussion about wikimedia projects. [On- line; accessed 25-May-2022].
12 [Poncelas et al. 2020] Poncelas, A., Lohar, P., Way, A., and Hadley, J. (2020). The impact of indirect machine translation on sentiment classification.
13 [Radford et al. 2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019). Language models are unsupervised multitask learners.
14 [Rajpurkar et al. 2018] Rajpurkar, P., Jia, R., and Liang, P. (2018). Know what you don’t know: Unanswerable questions for squad.
15 [Sanches. et al. 2022] Sanches., M., C. de S ́a., J., M. de Souza., A., Silva., D., R. de Souza., R., Reis., J., and Villas., L. (2022). Mccd: Generating human natural language conver- sational datasets. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,, pages 247–255. INSTICC, SciTePress.
16 [Sharir et al. 2020] Sharir, O., Peleg, B., and Shoham, Y. (2020). The cost of training nlp models: A concise overview.
17 [Souza et al. 2020] Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: Pretrained bert models for brazilian portuguese. In Cerri, R. and Prati, R. C., editors, Intelligent Systems, pages 403–417, Cham. Springer International Publishing.
18 [Vaswani et al. 2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need.
19 [Wagner et al. 2018] Wagner, J., Wilkens, R., Idiart, M., and Villavicencio, A. (2018). The brwac corpus: A new open resource for brazilian portuguese.
20 [Wang et al. 2018] Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R. (2018). Glue: A multi-task benchmark and analysis platform for natural language understanding.