A REVIEW ON QUESTION AND ANSWER SYSTEM FOR COVID-19 LITERATURE ON PRE-TRAINED MODELS

Abstract

The COVID-19 literature has accelerated at a rapid pace and the Artificial Intelligence community as well as researchers all over the globe has the responsibility to help the medical community. The CORD-19 dataset contains various articles about COVID-19, SARS-CoV-2, and related corona viruses. Due to massive size of literature and documents it is difficult to find relevant and accurate pieces of information. There are question answering system using pre-trained models and fine-tuning them using BERT Transformers. BERT is a language model that powerfully learns from tokens and sentence-level training. The variants of BERT like ALBERT, DistilBERT, RoBERTa, SciBERT alongwith BioSentVec can be effective in training the model as they help in improving accuracy and increase the training speed. This will also provide the information on using SPECTER-document level relatedness like CORD 19 embeddings for pre-training a Transformer language model. This article will help in building the question answering model to facilitate the research and save the lives of people in the fight against COVID 19.