BERT-based customer service question answering system

Abstract

A BERT-based customer service question and answer system belongs to the technical field of data calculation and comprises a receiving module, a preprocessing module, an intention module and a template engine module. The receiving module is used for receiving questions proposed by a user side; the preprocessing module is used for processing the received problem; the intention module is used for analyzing and acquiring the intention of the acquired problem; the template engine module is used for matching the obtained questions with standard questions to obtain question methods; an answer configuration module is used for generating answers for the questions provided by the system. According to the system, a BERT model is adopted for feature vector extraction; monitoring is carried out based on a triplet loss function of the Euclidean distance; compared with the adoption of a dichotomy cross entropy loss function, the generated vectors are more natural and reasonable in similarity distance calculation, and compared with a conventional training model, the triplet net simultaneously trains positive and negative samples, so that the model convergence is faster; meanwhile, the data in the system is in a closed-loop state, the modification period is shortened, and the accuracy of the system is improved.

Description

technical field [0001] The invention belongs to the technical field of data calculation, and in particular relates to a customer service question answering system based on BERT. Background technique [0002] Natural Language Processing (NLP) is the field where linguistics, computer science, and artificial intelligence interact. NLP is a branch of data science, the process of systematically analyzing, understanding and extracting information from text data in an intelligent and efficient manner. By using NLP and its components, a wide variety of text problems can be solved, such as text similarity, automatic summarization, machine translation, named entity recognition, relation extraction, sentiment analysis, and topic segmentation. [0003] Customer service is a profession that answers customers' questions and satisfies customers' reasonable demands. Its form is mainly text customer service, such as Taobao customer service, Jingdong customer service and so on. Customer se...

AI Technical Summary

Problems solved by technology

At present, the customer service question answering system is mainly implemented based on Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN). Text features are obtained through feature extraction of word vectors or word vectors. It is difficult to express the complete semantics of sentences, which affects the accuracy of the question answering system. sex

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