Customer Service Assist System based on Natural Language Processing

This paper proposes a novel assist system for customer service representatives based on natural language processing (NLP). In the proposed system, an NLP model calculates the relationships between a question from a customer and all the questions in a given FAQ list. Based on the model’s calculation, the system will recommend several FAQs that are more similar to the customer’s question than the others in the FAQ list and then the representative responses, whether the recommended questions are actually similar to the customer’s question or not. Since these responses become the data for the NLP model’s next training, the NLP model’s accuracy can be incrementally enhanced by repetitive fine-tuning with the accumulated data. The experimental result shows that the proposed system can effectively help customer service representatives as well as incrementally improve via the automatically accumulated data.