ChatParse: A New Application for Persian Bourse Chatbot

In this paper, we design and develop a brand new application for Persian stock-market chatbot using the retrieval approach namely ChatParse. The proposed architecture for this system consists of the Persian version of the BERT called ParsBERT in which we also add fully-connected and softmax layers to consider the number of classes according to our designed dataset. We manually design an appropriate Persian dataset for bourse application including 17 classes because we have found no Persian corpus for this application. ChatParse is able to have multi-turn conversations with users on the stock-market topic. The performance of the proposed system is evaluated in terms of accuracy, recall, precision, and F1-score on validation set. We also examine our application with test data acquired from users in real time. The average accuracy of the validation set over 17 classes is 68.29% showing the effectiveness of ChatParse as a new Persian Chatbot.