Deep Learning-based Question Answering System for Proactive Disaster Management

Kim, Young S.; Sohn, Jiu; Bang, Su Sik; Kim, Hyoungkwan

doi:10.22260/isarc2020/0181

Cited by 2 publications

(1 citation statement)

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“…We can take advantage of the latest advances in natural language processing (NLP) and question answering (QA) so as to better inform the population on environmental issues. While natural language processing of textual databases has been used to deal with environmental challenges [3,8,12], exploration of such databases in Brazilian Portuguese remains underdeveloped, to say the least.…”

Section: Introductionmentioning

confidence: 99%

DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

Cação,

José,

Oliveira

et al. 2021

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%