Deep Learning for Intelligent Customer Service Automation: Development of GRU, LSTM, and Recurrent Neural Network Architectures for Chatbot Applications

Natural language processing represents the most convenient and natural way for humans to interact and obtain information. As we enter the intelligent age, chatbots are becoming pivotal in facilitating human-machine interactions. This research investigates key technologies for chatbots to generate replies, given the increasing demand across sectors like small and medium-sized enterprises (SMEs). The study leverages deep learning frameworks to train an enterprise chatbot knowledge base. Ubuntu dialogue data was collected to generate a corpus for model training. The conversation dataset was preprocessed, word vectors trained, and dialogue Q&A converted for neural network processing. The Seq2Seq model combined with the Attention mechanism was utilised, comparing RNN, LSTM, and GRU recurrent neural networks, with LSTM selected as the encoder and decoder. Significantly, a Seq2seq model integrated the target corpus into the traditional Seq2Seq model to better suit customer service roles. A Flask web interface enabled users to input questions and displayed responses generated by the model. Notably, by dynamically loading the training dataset, the model can be hot-loaded and trained without restarting after deployment, enabling continuous improvement and adaptation to change customer needs.