用于客服辅助的对话模型研究
发布时间：2018-09-12 17:24
【摘要】：随着互联网经济的不断发展,提供在线商品和服务选购的电商平台的规模和成交量也在日益增大。这种改变的潮流对在线客服的服务质量和服务效率提出更高的要求。因此如何通过计算机技术来辅助人工客服提升其工作效率和工作质量是个值得研究的问题。在此基础上,本文围绕两种客服辅助技术展开:知识库查询服务和客服回复推荐服务,对相关的对话模型进行研究和探索。针对客服需要参考相关专业知识来完成高质量服务的需求,本文设计了一种知识库查询服务。该服务接受用户的自然语言问句作为输入,通过AIML模板匹配技术从输入中提取关键词和待查询属性,并返回知识库中对应的信息条目。传统匹配方式受制于自然语言表达的多样性,存在关键词匹配失效的问题。本文针对这个问题,提出了一种多轮迭代的同义词匹配算法,该算法提升了同义词的检出数量和准确度。针对如何提升人工客服工作效率的问题,本文提出了用于客服回复推荐的深度对话模型。本文从检索式深度对话模型和产生式对话模型两个方向来解决该问题。在检索式的深度对话模型中,本文设计了一种带上下文建模的对话模型,通过实验对比,其比不带上下文的对话模型有较大性能改善,在此基础上本文使用用户咨询的意图信息对模型进行了改善,获得了部分性能提升。在产生式对话模型中,本文设计了一种使用完整上下文用于预测客服对话的产生式对话模型,并在客服咨询数据集上同传统的Seq2Seq模型进行对比,该模型产生的回复效果更好。在产生式模型中,本文先实现了基础的Seq2Seq模型,用来作为对照,并根据本文提出的上下文编码方式提出并实现了对应的产生式对话模型。通过实验分析,我们发现本文提出的上下文建模方法对回复的推荐有提升效果。
[Abstract]:With the development of the Internet economy, the scale and volume of e-commerce platform for online goods and services are increasing day by day. This changing trend demands higher quality and efficiency of online customer service. Therefore, how to improve the efficiency and quality of manual customer service by computer technology is a problem worth studying. On this basis, this paper focuses on two kinds of customer service assistant technology: knowledge base query service and customer service response recommendation service, and studies and explores the relevant dialogue models. A knowledge base query service is designed to meet the needs of customer service which needs to refer to relevant professional knowledge to complete high quality service. The service takes user's natural language questions as input, extracts keywords and attributes from input by AIML template matching technique, and returns corresponding information items in the knowledge base. The traditional matching method is limited by the diversity of natural language expression, and there is the problem of keyword matching failure. In order to solve this problem, a multi-iteration synonym matching algorithm is proposed in this paper, which improves the number and accuracy of synonym detection. Aiming at the problem of how to improve the efficiency of artificial customer service, this paper presents an in-depth dialogue model for customer service response recommendation. This paper deals with this problem from two aspects: the retrieval depth dialogue model and the production dialogue model. In the retrieval model of deep dialogue, this paper designs a kind of dialogue model with context modeling. The experimental results show that the model has better performance than the model without context. On this basis, this paper improves the model by using the intention information of user consultation, and obtains some performance improvements. In the production dialogue model, this paper designs a production dialogue model which uses the complete context to predict the customer service dialogue, and compares it with the traditional Seq2Seq model on the customer service consultation data set. The response effect of the model is better than that of the traditional one. In the production model, we first implement the basic Seq2Seq model, which is used as a contrast, and propose and implement the corresponding production dialogue model according to the context encoding method proposed in this paper. Through experimental analysis, we find that the contextual modeling method proposed in this paper can improve the recommendation of response.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.1
本文编号：2239714
[Abstract]:With the development of the Internet economy, the scale and volume of e-commerce platform for online goods and services are increasing day by day. This changing trend demands higher quality and efficiency of online customer service. Therefore, how to improve the efficiency and quality of manual customer service by computer technology is a problem worth studying. On this basis, this paper focuses on two kinds of customer service assistant technology: knowledge base query service and customer service response recommendation service, and studies and explores the relevant dialogue models. A knowledge base query service is designed to meet the needs of customer service which needs to refer to relevant professional knowledge to complete high quality service. The service takes user's natural language questions as input, extracts keywords and attributes from input by AIML template matching technique, and returns corresponding information items in the knowledge base. The traditional matching method is limited by the diversity of natural language expression, and there is the problem of keyword matching failure. In order to solve this problem, a multi-iteration synonym matching algorithm is proposed in this paper, which improves the number and accuracy of synonym detection. Aiming at the problem of how to improve the efficiency of artificial customer service, this paper presents an in-depth dialogue model for customer service response recommendation. This paper deals with this problem from two aspects: the retrieval depth dialogue model and the production dialogue model. In the retrieval model of deep dialogue, this paper designs a kind of dialogue model with context modeling. The experimental results show that the model has better performance than the model without context. On this basis, this paper improves the model by using the intention information of user consultation, and obtains some performance improvements. In the production dialogue model, this paper designs a production dialogue model which uses the complete context to predict the customer service dialogue, and compares it with the traditional Seq2Seq model on the customer service consultation data set. The response effect of the model is better than that of the traditional one. In the production model, we first implement the basic Seq2Seq model, which is used as a contrast, and propose and implement the corresponding production dialogue model according to the context encoding method proposed in this paper. Through experimental analysis, we find that the contextual modeling method proposed in this paper can improve the recommendation of response.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.1
【参考文献】
相关硕士学位论文 前1条
1 朱旺南;基于本体的自动问答客服系统研究[D];青岛理工大学;2012年
，本文编号：2239714
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