Scholarlink

For the English question answering system, the BERT mode and the Generative Counter Networks (GANs) are optimized. The BERT model is used to understand the semantics of user questions and encode them so that they can extract more semantic information. On this basis, a new vector representation is given. By observing and modifying the BERT vector, the problem is understood better and the programming efficiency is improved. In addition, a query algorithm based on keywords and phrases is proposed to improve the accuracy of answers. On this basis, BERT learning algorithm is used to achieve multi-lingual collaborative work and improve the accuracy of translation. Productive policy optimization techniques are designed to generate more diverse and realistic responses. Through the confrontation training, the quality and accuracy of the answers to the questions are effectively improved. Experimental results show that the proposed algorithm can effectively improve translation quality, BLEU score and computational efficiency. The results of this project will provide new ideas for English question answering system in semantic understanding, translation quality, diversity and user experience.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Translation and Generation Optimization Strategies in English Question Answering Systems Based on BERT and Generative Adversarial Networks

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Lijun Wang

摘要

For the English question answering system, the BERT mode and the Generative Counter Networks (GANs) are optimized. The BERT model is used to understand the semantics of user questions and encode them so that they can extract more semantic information. On this basis, a new vector representation is given. By observing and modifying the BERT vector, the problem is understood better and the programming efficiency is improved. In addition, a query algorithm based on keywords and phrases is proposed to improve the accuracy of answers. On this basis, BERT learning algorithm is used to achieve multi-lingual collaborative work and improve the accuracy of translation. Productive policy optimization techniques are designed to generate more diverse and realistic responses. Through the confrontation training, the quality and accuracy of the answers to the questions are effectively improved. Experimental results show that the proposed algorithm can effectively improve translation quality, BLEU score and computational efficiency. The results of this project will provide new ideas for English question answering system in semantic understanding, translation quality, diversity and user experience.