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The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic'of health information online. This has motivated the need for accurate semanticsearch and retrieval of reliable COVID-19 information across millions ofdocuments, in multiple languages. To address this challenge, this paperproposes a novel high precision and high recall neural Multistage BiCrossencoder approach. It is a sequential three-stage ranking pipeline which usesthe Okapi BM25 retrieval algorithm and transformer-based bi-encoder andcross-encoder to effectively rank the documents with respect to the givenquery. We present experimental results from our participation in theMultilingual Information Access (MLIA) shared task on COVID-19 multilingualsemantic search. The independently evaluated MLIA results validate our approachand demonstrate that it outperforms other state-of-the-art approaches accordingto nearly all evaluation metrics in cases of both monolingual and bilingualruns.

Multistage BiCross encoder for multilingual access to COVID-19 health information