| dc.description.abstract | Natural language processing technology (NLP) has a very important position in the application of artificial intelligence, and its application range is also very extensive, including mutual translation of languages, giving appropriate tags to articles, using writing methods to identify authors, and dialogue robots. A smart robot that can assist in answering customer questions cannot only help reduce the cost of the enterprise, but also solve the 7 X 24 hours uninterrupted service quality. However, the knowledge base answering robot constructed with conditional methods can only meet 60% of the requirements. How to improve the response rate and accuracy of the customer’s questions is also a model that this research wants to put forward.
This study takes the telecommunications industry as an example. Since the customer service of the telecommunications industry must have a variety of knowledge areas including network issues, mobile phone issues, contract issues, billing issues, collection issues, and there are numerous problems, this experiment uses a two-stage dialogue learning framework, and In the second stage, the question task conditions and guest attributes are added as the answer content to achieve an improved response rate and accuracy rate.
The results of the experiment have been verified by two text generation evaluation methods. The model trained after adding question classification and customer attributes can improve the accuracy of the response, thereby increasing the response rate. | en_US |