具有自主学习与记忆功能的智能政务问答系统研究

Research on intelligent government question answering system with autonomous learning and memory function

任务型问答系统一旦构建好,通常是固定不变的,能回答的问题非常有限,难以满足用户的需求。对此,提出一种自动实时更新知识库的方法,当用户提了一个问答系统回答不了的问题,系统会把该问题自动发送给人工客服,人工客服利用专业知识回复后,系统能够自动实时获取用户提的问题和人工客服回复的答案,并把这个问答对自动实时更新到知识库,之后如果其他用户提了类似的问题,问答系统就能够快速给出对应的答案。以政务领域的问答系统为例,应用文本向量化方法 ERNIE构建知识库自动实时更新的问答系统。经过计算机实验证明,提出的方法能够实现知识库自动实时更新,构建的问答系统具有自主学习与记忆功能,提高了任务型问答系统的智能化水平。

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.