A COVID-19 Search Engine (CO-SE) with Transformer-based Architecture

TL;DR

This paper introduces CO-SE, a Transformer-based COVID-19 search engine that efficiently retrieves and answers complex queries from a large corpus of scientific publications, outperforming previous models.

Contribution

The paper presents a novel hybrid system combining TF-IDF retrieval with Transformer-based reading for COVID-19 literature search and question answering.

Findings

Achieved an exact match ratio of 71.45%

Secured a semantic answer similarity score of 78.55%

Outperformed existing models on benchmark datasets

Abstract

Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF-IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.