Exploring the State of Natural language Processing: A Survey of Recent Advances, Challenges and Future Scope

Abstract

Abstract: Recent developments in deep learning have largely been responsible for the excellent performance on benchmark datasets of natural language processing algorithms. These advancements have greatly enhanced the capabilities of NLP systems utilised in programmes like sentiment analysis, speech recognition, and virtual assistants. However, the vulnerability of these systems to adversarial attacks has revealed the limitations in their robustness and language understanding abilities, which poses challenges when deploying NLP systems in real-world scenarios. This study provides a thorough analysis of studies on NLP robustness, providing a structured overview across multiple dimensions. We explore a variety of robustness-related topics, including as methodologies, measures, embedding, and benchmarking. Moreover, it emphasizes the need for a multidimensional perspective on robustness and offer insights into ongoing research efforts while identifying gaps in the literature. We propose potential directions for future exploration aimed at addressing these gaps and enhancing the robustness of NLP systems.