An Open-Domain Search Quiz Engine Based on Transformer

the volume of information on the Internet continues to grow exponentially, efficient retrieval of relevant data has become a significant challenge. Traditional keyword matching techniques, while useful, often fall short in addressing the complex and varied queries users present. This paper introduces a novel approach to automated question and answer systems by integrating deep learning and natural language processing (NLP) technologies. Specifically, it combines the Transformer model with the HowNet knowledge base to enhance semantic understanding and contextual relevance of responses. The proposed system architecture includes layers for word embedding, Transformer encoding, attention mechanisms, and Bi-directional Long Short- Term Memory (Bi-LSTM) processing, enabling sophisticated semantic matching and implication recognition. Using the BQ Corpus dataset in the banking and finance domain, the system demonstrated substantial improvements in accuracy and F1-score over existing models. The primary contributions of this research are threefold: (1) the introduction of a semantic fusion approach using HowNet for enhanced contextual understanding, (2) the optimization of Transformer-based deep learning techniques for Q&A systems, and (3) a comprehensive evaluation using the BQ Corpus dataset, demonstrating significant improvements in accuracy and F1-score over baseline models. These contributions have important implications for improving the handling of complex and synonym-rich queries in automated Q&A systems. The experimental results highlight that the integrated approach significantly enhances the performance of automated Q&A systems, offering a more efficient and accurate means of information retrieval. This advancement is particularly crucial in the era of big data and Web 3.0, where the ability to quickly and accurately access relevant information is essential for both users and organizations.