Transformer-Based Word Sense Disambiguation: Advancements, Impact, and Future Directions

Word Sense Disambiguation (WSD) is a challenging field of research in natural language processing. Enhanced WSD techniques have the potential to significantly enhance the performance of various Natural Language Processing tasks, such as text translation, question answering, sentiment analysis, and text generation, among others. Recently, the advent of novel deep learning techniques, particularly transformers, has yielded noteworthy advancements in the field of Natural Language Processing and its associated sub-tasks. Today, AI-driven tools pave a new era of technology by harnessing the transformative architecture of transformers as a fundamental component. Transformers have presumed a pivotal role in numerous NLP tasks, notably in WSD, and their significance continues to grow as they progress. This paper presents a comprehensive study that explores the advancements achieved in addressing the problem of word sense disambiguation, with a specific focus on the utilization of transformer models.