Intelligent Conversational Agent for Medical Information
摘要
In this paper we address the problem of automating the process of handling the numerous inquiries received by the medical information teams from the users in the pharmaceutical industry. Our approach foresees the development of a holistic system which includes an intelligent conversational agent that is informed by a set of questions and answers (Q&A), extracted from a large corpus of medical scientific documents in a semi-automatic manner. We investigate two different methods (i.e., template-based and neural-based) for extracting Q&A pairs that are subsequently used to train the Natural Language Understanding model of the conversational agent. Both methods are qualitatively evaluated by experts of a medical information team. The performance of our system shows that our approach is robust with promising results which can reach an average performance of 64%.