基于深度语义的三阶段式问题检索模型  

Three-stage Problem Retrieval Model Based on Deep Semantics

作　　者：赵钊 尚爱国 焦一凯 朱欣娟[1] ZHAO Zhao;SHANG Ai-Guo;JIAO Yi-Kai;ZHU Xin-Juan(School of Computer Science,Xi’an Polytechnic University,Xi’an 710600,China)

机构地区：[1]西安工程大学计算机科学学院,西安710600

出　　处：《计算机系统应用》2023年第5期244-252,共9页Computer Systems & Applications

基　　金：国家重点研发计划(2019YFC1521405)。

摘　　要：随着检索式问答技术的日趋成熟,如何有效利用现有的模型和检索工具,达到问答系统的整体优化,是亟待研究的现实问题.提出了一种基于深度语义的三阶段式问题检索模型(TSFR-RM),用于构筑智能客服问答系统.首先基于深度学习方法计算用户问题和知识库问题的文本表征相似度,锁定top-k候选答案集,同时赋予模型泛化检索的能力;其次针对用户问题与知识库问题答案对,构造多角度语义特征,进行精确比对计算;最后构造状态预测模型返回问题检索精准答案.通过真实文旅机构客服问答数据集实验及实际应用效果表明,该模型相较于其他基于特征和表征的问题检索模型,在精确率(precision)性能指标上最高提升9.3个百分点,提升优化了智能客服检索系统的准确性.As the retrieval-based question and answer(Q&A)technology becomes increasingly mature,determining how to effectively use existing models and retrieval tools to achieve overall optimization of Q&A systems is a practical problem that needs to be studied.The study proposes a three-stage and a fusion of feature and representation problem retrieval model(TSFR-RM)for constructing intelligent customer service Q&A systems.Firstly,the similarity between the text representations of users’questions and questions in knowledge bases is calculated by deep learning methods to target the top-k candidate answer set and give the model the ability of generalized retrieval.Secondly,multi-angle semantic features are constructed for pairs of answers to users’questions and questions in knowledge bases to perform accurate comparison calculations.Finally,a state prediction model is built to return accurate answers to question retrieval.The experimental and practical application results show that the model improves the accuracy of the intelligent customer service retrieval system for a cultural tourism institution by up to 9.3 percentage points in the performance index of precision compared with other feature-and representation-based question retrieval models.

关 键 词：智能客服系统 问题精确检索 多阶段任务学习 多角度语义特征 问题状态判断 语义分割 数字经济 问答系统 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]