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Text classification problem is a very broad field of study in the field ofnatural language processing. In short, the text classification problem is todetermine which of the previously determined classes the given text belongs to.Successful studies have been carried out in this field in the past studies. Inthe study, Bidirectional Encoder Representations for Transformers (BERT), whichis a frequently preferred method for solving the classification problem in thefield of natural language processing, is used. By solving classificationproblems through a single model to be used in a chatbot architecture, it isaimed to alleviate the load on the server that will be created by more than onemodel used for solving more than one classification problem. At this point,with the masking method applied during the estimation of a single BERT model,which was created for classification in more than one subject, the estimationof the model was provided on a problem-based basis. Three separate data setscovering different fields from each other are divided by various methods inorder to complicate the problem, and classification problems that are veryclose to each other in terms of field are also included in this way. Thedataset used in this way consists of five classification problems with 154classes. A BERT model containing all classification problems and other BERTmodels trained specifically for the problems were compared with each other interms of performance and the space they occupied on the server.

Profitable Trade-Off Between Memory and Performance In Multi-Domain Chatbot Architectures