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Natural language processing is an important part of human-machine applications, and it is also a benchmark that is often used to show results in artificial intelligence. In recent years, with the development of chatbots, it is realized that the technology of human-machine dialogue has strong commercial value. As deep learning technology continues to advance, it also advances the development of talking to machines. A chat machine is a program that simulates human behavior through natural language to make it talk to humans, including communication channels such as text and voice.
This paper uses chatbots to improve the process of the existing service sales system, and uses a task-oriented dialogue model architecture in the dialogue strategy, allowing users to chat with chatbots in a question-and-answer manner, simplifying the completion of case establishment, quotation, and finding relevant products, etc. Inquire about the user's product requirements or answer questions by current telephone or mail. This manual reply is likely to cause the customer to wait too long or the lack of knowledge of the staff, which makes it impossible to correctly reply to the customer's question.
Therefore, this paper uses chatbots to improve and optimize the customer service process of small and medium-sized enterprises, uses existing product information and data at home and abroad, and decomposes and analyzes the dialogue with customers, and then recommends the corresponding products or answers to customers according to the dialogue. corresponding question. Under the existing manpower, one-to-many customer service can be achieved, which not only solves the problem of manpower shortage, but also solves the poor consumer experience caused by the inability to respond to customers in a timely manner.
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