Research on intelligent government question answering system with autonomous learning and memory function - Fang Haiquan; Deng Mingming (Dianzi Jishu Yingyong, 2024)
Once a task-based question answering system is built, it is usually fixed and can answer very limited questions, making it difficult to meet user needs. A method for automatically updating the knowledge base in real-time was proposed. When a user asks a question that the question answering system cannot answer, the system will automatically send the question to the manual customer service. After the manual customer service used professional knowledge to reply, the system can automatically obtain the user's question and the answer replied by the manual customer service in real time, and automatically update the question answering pair to the knowledge base in real time. If other users ask similar questions, the question answering system can quickly provide corresponding to answers. Taking the question answering system in the field of government affairs as an example, the text vectorization method ERNIE was applied to build a question answering system that automatically updates the knowledge base in real time. After computer experiments, it has been proven that the proposed method can achieve automatic real-time updates of the knowledge base, and the constructed question answering system has autonomous learning and memory functions, improving the intelligence level of the task-based question answering system.