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DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

This repository is related to the paper DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment, by Flávio Nakasato Cação¹, Marcos Menon José¹, André Seidel Oliveira¹, Stefano Spindola¹, Anna Helena Reali Costa¹ and Fábio Gagliardi Cozman¹. It was accepted at the Main Track of the 10th Brazilian Conference on Intelligent System (BRACIS'21).

Abstract: The challenge of climate change and biome conservation is one of the most pressing issues of our time - particularly in Brazil, where key environmental reserves are located. Given the availability of large textual databases on ecological themes, it is natural to resort to question answering (QA) systems to increase social awareness and understanding about these topics. In this work, we introduce multiple QA systems that combine in novel ways the BM25 algorithm, a sparse retrieval technique, with PTT5, a pre-trained state-of-the-art language model. Our QA systems focus on the Portuguese language, thus offering resources not found elsewhere in the literature. As training data, we collected questions from open-domain datasets, as well as content from the Portuguese Wikipedia and news from the press. We thus contribute with innovative architectures and novel applications, attaining an F1-score of 36.2 with our best model.

Link for the paper: https://link.springer.com/chapter/10.1007/978-3-030-91699-2_29.

Citing

Cite this paper as:

Cação F.N., José M.M., Oliveira A.S., Spindola S., Costa A.H.R., Cozman F.G. (2021) DEEPAGÉ: Answering Questions in Portuguese About the Brazilian Environment. In: Britto A., Valdivia Delgado K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science, vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_29

Acknowledgments

This work was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Finance Code 001) and by the Itaú Unibanco S.A., through the Programa de Bolsas Itaú (PBI) of the Centro de Ciência de Dados (C 2 D) of Escola Politécnica of Universidade de São Paulo (USP). We also gratefully acknowledge support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (grants 312180/2018-7 and 310085/2020-9) and the Center for Artificial Intelligence (C4AI-USP), with support by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant 2019/ 07665-4) and by the IBM Corporation.

¹ Polytechnic School, University of Sao Paulo, Sao Paulo, Brazil