Enhancing Rasa NLU model for Vietnamese chatbot

Trang Nguyen, Maxim Shcherbakov


Nowadays, the use of chatbots in industry and education has increased substantially. Building the chatbot system using traditional methods less effective than the applied of machine learning (ML) methods. Before chatbot based on finite-state, rule-base, knowledgebase, etc, but these methods still exist limitation. Recently, thanks to the advancement in natural language processing (NLP) and neural network (NN), conversational AI systems have made significant progress in many tasks such as intent classification, entity extraction, sentiment analysis, etc. In this paper, we implemented a Vietnamese chatbot for COVID-19 information that is capable of understanding natural language. It can generate responses, take actions to the user and remember the context of the conversation. We proposed Rasa platform for building chatbot and presented a method using custom pipeline for NLU model. In our work, we applied the pre-trained language models FastText and BERT and created custom tokenizer for our own pipelines. The application of pre-trained language models for NLU model has shown better results than the training model from scratch.

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