An Analysis of Pairwise Question Matching with Machine Learning

Abstract

n the realm of Natural Language Processing (NLP) and machine learning, lies the challenging quest to detect duplicate question pairs with semantic precision. Our research endeavors to craft a cutting-edge model capable of discerning whether two questions, despite their divergent phrasing, spelling, or grammatical variations, share a common intent on digital forums or search engines. A paramount facet of this study involves the creation and training of an exemplary model using a meticulously curated dataset of labeled question pairs, each annotated as either duplicates or distinct entities. By leveraging state-of-the-art NLP techniques, we aspire to build an exceptionally accurate model that will revolutionize the user search experience by facilitating the identification of duplicate questions. This pioneering research paves the way for a more refined and enhanced approach to tackle the challenges of semantic similarity in the context of question pairs