Optimizing Customer Interactions: A BERT and Reinforcement Learning Hybrid Approach to Chatbot Development

In the case of chatbots massive progress has been made, but problems remain in handling the complexity of the sentence and in context relevance. Traditional models can be rather insufficient when it comes to providing various levels of detail in the responses to the end-users’ questions, particularly when referring to customer support scenarios. To overcome these limitations, this research comes up with a new model which combines the BERT model with DRL. Through DRL, BERT pre-training is adding flexibility and correspondence to correctly perceive contextual delicate matters in the response. The proposed method includes the following pipeline where in; data tokenization, conversion to lowercase characters, lemmatization and then passes through the BERT fine-tuned model. DRL is utilized to optimize the chatbot’s response in the light of long term rewards and the conversational history, the interactions are formulated as a Markov Decision Process with the reward functions based on cosine similarity of the consecutive responses. This makes it feasible for the chatbot to provide context based replies in addition to the option of constant learning for enhanced performance. It also proved that the accuracy and relevance of the BERT-DRL hybrid system were higher than traditional models according to the BLEU and ROUGE scores. The performance of the chatbot also increases with the length of the conversation and the transitions from one response to the other are coherent. This research contributes to the field through the integration of BERT in understanding language and DRL in the iterative learning process in the innovation within the flaws of chatbot technologies and establishing a new benchmark for conversational AI in customer service settings.

@article{Praneeth2024,
  title     = {Optimizing Customer Interactions: A BERT and Reinforcement Learning Hybrid Approach to Chatbot Development},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {K. R. Praneeth and Taranpreet Singh Ruprah and J Naga Madhuri and A L Sreenivasulu and Syed Shareefunnisa and Vuda Sreenivasa Rao},
  doi       = {10.14569/IJACSA.2024.0150958},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150958}
}