  With the surge of pretrained language models, a new pathway has been opened
to incorporate Persian text contextual information. Meanwhile, as many other
countries, including Iran, are fighting against COVID-19, a plethora of
COVID-19 related articles has been published in Iranian Healthcare magazines to
better inform the public of the situation. However, finding answers in this
sheer volume of information is an extremely difficult task. In this paper, we
collected a large dataset of these articles, leveraged different BERT
variations as well as other keyword models such as BM25 and TF-IDF, and created
a search engine to sift through these documents and rank them, given a user's
query. Our final search engine consists of a ranker and a re-ranker, which
adapts itself to the query. We fine-tune our models using Semantic Textual
Similarity and evaluate them with standard task metrics. Our final method
outperforms the rest by a considerable margin.
