  The current COVID-19 pandemic has lead to the creation of many corpora that
facilitate NLP research and downstream applications to help fight the pandemic.
However, most of these corpora are exclusively for English. As the pandemic is
a global problem, it is worth creating COVID-19 related datasets for languages
other than English. In this paper, we present the first manually-annotated
COVID-19 domain-specific dataset for Vietnamese. Particularly, our dataset is
annotated for the named entity recognition (NER) task with newly-defined entity
types that can be used in other future epidemics. Our dataset also contains the
largest number of entities compared to existing Vietnamese NER datasets. We
empirically conduct experiments using strong baselines on our dataset, and find
that: automatic Vietnamese word segmentation helps improve the NER results and
the highest performances are obtained by fine-tuning pre-trained language
models where the monolingual model PhoBERT for Vietnamese (Nguyen and Nguyen,
2020) produces higher results than the multilingual model XLM-R (Conneau et
al., 2020). We publicly release our dataset at:
https://github.com/VinAIResearch/PhoNER_COVID19
