Building a Chatbot: Architecture Models and Text Vectorization Methods
Abstract
In this paper, we review the recent progress in developing intelligent conversational agents (or chatbots), its current architectures (rule-based, retrieval based and generative-based models) and discuss the main advantages and disadvantages of the approaches. Additionally, we conduct a comparative analysis of state-of-the-art text data vectorization methods which we apply in implementation of a retrieval-based chatbot as an experiment. The results of the experiment are presented as a quality of the chatbot responses selection using various R10@k measures. We also focus on the features of open data sources providing dialogs in Russian. Both the final dataset and program code are published. The authors also discuss the issues of assessing the quality of chatbots response selection, in particular, emphasizing the importance of choosing the proper evaluation method.
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