  The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic'
of health information online. This has motivated the need for accurate semantic
search and retrieval of reliable COVID-19 information across millions of
documents, in multiple languages. To address this challenge, this paper
proposes a novel high precision and high recall neural Multistage BiCross
encoder approach. It is a sequential three-stage ranking pipeline which uses
the Okapi BM25 retrieval algorithm and transformer-based bi-encoder and
cross-encoder to effectively rank the documents with respect to the given
query. We present experimental results from our participation in the
Multilingual Information Access (MLIA) shared task on COVID-19 multilingual
semantic search. The independently evaluated MLIA results validate our approach
and demonstrate that it outperforms other state-of-the-art approaches according
to nearly all evaluation metrics in cases of both monolingual and bilingual
runs.
