BERT: advancements in language understanding for different NLP tasks: challenges and future perspectives

Abstract Natural Language Processing (NLP) has significantly evolved from computational linguistics, enabling computers to comprehend the structure and meaning of human language, by better extracting data or information from text-based documents and enhancing performance on more complex analytics tasks. This review explores advancements, challenges, and future perspectives of language understanding in various NLP tasks, with a special emphasis on the Bidirectional Encoder Representations from Transformers (BERT) model. The progress of BERT has revolutionized a new era in the perception of natural languages in a way that computers can better process human language. Compared to other language representation models such as ELMo and traditional transformer-based architectures, BERT demonstrates significant advancements in performance and understanding of human language. The purpose of this paper is to provide an in-depth discussion on the BERT model including its basic concept, architectural structure, method of training, and application in different NLP tasks in the real world, emphasizing its importance in enhancing NLP research. In this study, we focused on recent studies of high outcomes that increase effectiveness and understanding of language models in NLP task and optimize the efficiency of the language model. This paper aims to provide readers with a better knowledge of the BERT language model’s impact and the future directions of its application to different NLP tasks.