  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.
