Application and Effectiveness of BERT in Question and Answer Modelling

In the field of Artificial Intelligence, chat Question and Answer (Q&A) systems represent a core application that simulates human dialogue capabilities. Given the quick development of natural language processing (NLP) technology, chat Q&A models according to Bidirectional Encoder Representations from Transformers (BERT) have emerged as a significant research focus. The BERT model, known for its deep bi-directional representations, enhances Q&A systems with a level of semantic comprehension that previous models struggled to achieve. This review aims to explore recent research progress in BERT-based chat Q&A models and analyze key issues and challenges encountered in their practical applications. This paper begins by introducing the background and principles of the BERT model, highlighting its importance in natural language processing. Then, the paper reviews in detail the key techniques and approaches of BERT-based chat Q&A models. The purpose of this thorough summary is to provide readers a clear grasp of BERT's role in chat Q&A systems, and the challenges it faces, and ultimately advance research and application development in this domain.