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English Information

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Authors
# Name
1 Pedro Jardim(pedrocjardim@usp.br)
2 Leonardo Moraes(leo.mauro.desenv@gmail.com)
3 Cristina Aguiar(cdac@icmc.usp.br)

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

Reference
# Reference
1 Athira, P., Sreeja, M., and Reghuraj, P. (2013). Architecture of an ontology-based domain- specific natural language question answering system. International Journal of Web & Semantic Technology, 4(4): article number 31.
2 Bartolo, M., Roberts, A., Welbl, J., Riedel, S., and Stenetorp, P. (2020). Beat the ai: Investigating adversarial human annotation for reading comprehension. Transactions of the Association for Computational Linguistics, 8:662–678.
3 Beal, R., Norman, T. J., and Ramchurn, S. D. (2019). Artificial intelligence for team sports: a survey. The Knowledge Engineering Review, 34:e28.
4 Hill, F., Bordes, A., Chopra, S., and Weston, J. (2016). The goldilocks principle: Reading children’s books with explicit memory representations. arXiv preprint arXiv:1511.02301.
5 Karpukhin, V., O ̆guz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t. (2020). Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906.
6 Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Kelcey, M., Devlin, J., Lee, K., Toutanova, K. N., Jones, L., Chang, M.-W., Dai, A., Uszkoreit, J., Le, Q., and Petrov, S. (2019). Natural questions: a benchmark for question answering research. Transactions of the Association of Com- putational Linguistics.
7 Lai, G., Xie, Q., Liu, H., Yang, Y., and Hovy, E. (2017). Race: Large-scale reading comprehension dataset from examinations. arXiv preprint arXiv:1704.04683.
8 Liu, Q., Jiang, S., Wang, Y., and Li, S. (2020). LiveQA: A question answering dataset over sports live. In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 1057–1067, Haikou, China. Chinese Information Processing Society of China.
9 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettle- moyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized BERT pretraining approach. CoRR, abs/1907.11692.
10 Mishra, A. and Jain, S. K. (2016). A survey on question answering systems with clas- sification. Journal of King Saud University - Computer and Information Sciences, 28(3):345–361.
11 Mittell, J. (2009). Sites of participation: Wiki fandom and the case of lostpedia. Trans- formative Works and Cultures, 3(3):1–10.
12 Moraes, L. M. P., Jardim, P., and Aguiar, C. D. (2023). Design principles and a software reference architecture for big data question answering systems. In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS), pages 57– 67. INSTICC, SciTePress.
13 Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., and Deng, L. (2016). MS MARCO: A human generated machine reading comprehension dataset. CoRR, abs/1611.09268.
14 Pan, A., Chan, J. S., Zou, A., Li, N., Basart, S., Woodside, T., Ng, J., Zhang, H., Emmons, S., and Hendrycks, D. (2023). Do the rewards justify the means? measuring trade-offs between rewards and ethical behavior in the machiavelli benchmark.
15 Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR, abs/1910.10683.
16 Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. arXiv e-prints, page arXiv:1606.05250.
17 Ribeiro, M. R., Barioni, M. C. N., de Amo, S., Roncancio, C., and Labb ́e, C. (2017). Soccer2014ds: a dataset containing player events from the 2014 world cup. In XXXII Simp ́osio Brasileiro de Banco de Dados: Dataset Showcase Workshop, SBBD 2017, pages 278–285, Uberlˆandia, MG, Brazil. SBC.
18 Richardson, M., Burges, C. J., and Renshaw, E. (2013). MCTest: A challenge dataset for the open-domain machine comprehension of text. In Proceedings of the 2013 Confer- ence on Empirical Methods in Natural Language Processing, pages 193–203, Seattle, Washington, USA. Association for Computational Linguistics.
19 Alvares, J. C. M. and Ribeiro, M. R. (2019). Soccernews2018: a dataset of statistics and news of the 2018 brazilian soccer championship. In XXXIV Simp ́osio Brasileiro de Banco de Dados: Dataset Showcase Workshop, SBBD 2019, pages 440–446, Fort- aleza, CE, Brazil. SBC.