Singh, I., Scarton, C. orcid.org/0000-0002-0103-4072 and Bontcheva, K. orcid.org/0000-0001-6152-9600 (2021) Multistage BiCross encoder for multilingual access to COVID-19 health information. PLoS ONE, 16 (9). e0256874.
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Jul 2021 07:06 |
Last Modified: | 13 Sep 2021 08:38 |
Status: | Published |
Publisher: | Public Library of Science |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0256874 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176611 |