A GOAL-ORIENTED CHATBOT BASED ON MACHINE LEARNING

Nowadays chatbots are becoming very popular in many areas, such as business, banking, healthcare, study, travel tips, etc. The popularity of messaging platforms such as Telegram, Messenger, Whatsapp, and others has made chatbots not only popular but also become a trend in the future. Since the end of December 2019, the onset of the COVID-19 pandemic has brought about a major global health crisis. Therefore, it is extremely important to provide information about the epidemic to all people. Many governments and organizations have launched chatbots to inform the public about COVID-19. However, these chat rules are limited as they understand a limited set of questions entered by users. Thereby, creating a chatbot based on machine learning for coronavirus information is an urgent task. The purpose of the study is the development of a chatbot for searching for information about COVID-19 coronavirus infection. The method of designing and developing a chatbot on the RASA framework, as well as testing of the developed prototype, are described. Three chatbot models were created: the baseline model (B), the baseline model with synonyms (BS), and the baseline model with synonyms and noises (BSS). The effectiveness of three models was evaluated based on the following indicators: accuracy, precision, and F-measure. The analysis results showed that the BS and BSS models are better than the B model.

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