  For the last two years, from 2020 to 2021, COVID-19 has broken disease
prevention measures in many countries, including Vietnam, and negatively
impacted various aspects of human life and the social community. Besides, the
misleading information in the community and fake news about the pandemic are
also serious situations. Therefore, we present the first Vietnamese
community-based question answering dataset for developing question answering
systems for COVID-19 called UIT-ViCoV19QA. The dataset comprises 4,500
question-answer pairs collected from trusted medical sources, with at least one
answer and at most four unique paraphrased answers per question. Along with the
dataset, we set up various deep learning models as baseline to assess the
quality of our dataset and initiate the benchmark results for further research
through commonly used metrics such as BLEU, METEOR, and ROUGE-L. We also
illustrate the positive effects of having multiple paraphrased answers
experimented on these models, especially on Transformer - a dominant
architecture in the field of study.
