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.
