Automating Customer Service Using Natural Language Processing

The integration of Natural Language Processing (NLP), particularly employing transformer-based models, has revolutionized customer service automation in recent years. This study explores the transformative impact of transformer-based NLP models on customer service interactions, addressing the historical evolution of customer service automation and the pivotal role played by these advanced NLP technologies. Through a comprehensive analysis of three distinct models—T5, GPT2, and LSTM—this study evaluates their effectiveness in enhancing customer service interactions. The T5 model demonstrates remarkable performance, achieving perfect training and validation accuracy while maintaining low training and validation losses. Its predictions closely match the ground truth values for the validation set, indicating superior predictive accuracy. The GPT2 model exhibits competitive performance, albeit with higher overall mean squared error (MSE) and mean absolute error (MAE) values compared to the T5 model. Meanwhile, the LSTM model showcases competitive performance with significantly lower computational requirements. Despite the advancements, challenges persist in acquiring real-world datasets for training. This study illuminates the potential for transformer-based NLP models to enhance user experiences, scalability, and efficiency in automated customer service across various industries, providing valuable insights for future research and development efforts.