As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing custom...As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.展开更多
为提高电力客服服务质量,提出一种电力智能客服问答系统。基于卷积神经网络(convolutionalneural networks,CNN)和双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络提取和表示重要信息和上下文信息;结合BiLSTM网络和...为提高电力客服服务质量,提出一种电力智能客服问答系统。基于卷积神经网络(convolutionalneural networks,CNN)和双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络提取和表示重要信息和上下文信息;结合BiLSTM网络和协同注意机制,提取语义信息并进一步表示为特征向量,解决长语句中前后词之间的依赖问题,获得问题对之间的相关特征表示;提出一种将余弦相似性和欧氏距离进行调和的相似性计算函数,实现问题对的高效匹配;以某电力公司提供的电力数据为例,对所提模型进行实验验证。结果表明:所提模型性能最优,准确率和召回率分别为90.96%和88.63%,为电力客服智能服务的发展提供了一定借鉴作用。展开更多
基金supported by National Natural Science Foundation of China(No.2018YFB0905000).
文摘As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.
文摘为提高电力客服服务质量,提出一种电力智能客服问答系统。基于卷积神经网络(convolutionalneural networks,CNN)和双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络提取和表示重要信息和上下文信息;结合BiLSTM网络和协同注意机制,提取语义信息并进一步表示为特征向量,解决长语句中前后词之间的依赖问题,获得问题对之间的相关特征表示;提出一种将余弦相似性和欧氏距离进行调和的相似性计算函数,实现问题对的高效匹配;以某电力公司提供的电力数据为例,对所提模型进行实验验证。结果表明:所提模型性能最优,准确率和召回率分别为90.96%和88.63%,为电力客服智能服务的发展提供了一定借鉴作用。
