Design and Implementation: Deep Learning-based Intelligent Chatbot

Currently, chatbots face the issues such as inadequate use of multi-modality information, poor feelings of expression competence, and a lack of a model fusion mechanism. Deep learning applications include NLP-based "natural language understanding (NLU)", sentiment analysis, machine translation, and word vector representation. People have started looking into the new capabilities of chatbots and applying deep learning techniques to chatbots. Provided the actual problem that conventional chatbots Seq-2-Seq model training tends to produce quite secure, widely accepted, and repetitive responses to the speech corpus, this article utilizes the understanding of cooperative pieces of evidence to develop the optimization problem of the model in order to generate better and much more versatile response by the model and can provide to the client with a better and expected and quality outcome. Results of the experiment revealed that the model was found to outperform the single model in many indicators in the short text given data experiment.