References validation in scholarly articles using RoBERTa

Abstract

In the recent era of technological advancement, evaluating references and assignments and validating those are some of the primary processes to justify the authenticity of a research work in the academic sector. The purpose of referencing is to ensure ethical writing and to make the search easier in a particular area for the reader when it is accurate. The traditional way to cross-check the validations is to check them manually one by one, which is above argument, equivalent to an- other work, and sometimes so tiring that the reader loses interest in reading the original paper. The target of this research paper is to introduce a semi-automatic digital system that enables researchers to justify the references used in a research paper without doing it manually. In this model, we have prepared a sentence trans- former named RoBERTa for generating embedding. We sanitize and preprocess an entire research paper and cross-match that against a reference query using the proposed model in terms of finding semantic and contextual similarity. The result shows mostly similar contexts based on the similarity check. We have compared the embedding of query and user input articles with the help of K similar search function. Our model outperformed the existing BERT and SBERT models’ output in accuracy with a F1 score of 0.777, which establishes the fact that the model can be used in real life with a simple query of text from research articles.