AidIR: An Interactive Dialog System to Aid Disease Information Retrieval

Abstract

This paper proposes an interactive dialog system, called AidIR, to aid information retrieval. AidIR allows users to retrieve information on diseases resulting from coronaviruses and diseases transmitted by vector mosquitoes with natural language interaction and Line chat media. In a subjective evaluation, we asked 20 users to rate the intuitiveness, usability, and user experience of AidIR with a range between −2 and 2. Moreover, we also asked these users to answer yes–no questions to evaluate AidIR and provide feedback. The average scores of intuitiveness, usability, and user experience are 0.8, 0.8, and 1.05, respectively. The yes–no questions demonstrated that AidIR is better than systems using the graphical user interface in mobile phones and single-turn dialog systems. According to user feedback, AidIR is more convenient for information retrieval. Moreover, we designed a new loss function to jointly train a BERT model for domain classification and sequence label tasks. The accuracy of both tasks is 92%. Finally, we trained the dialog policy network with supervised learning tasks and deployed the reinforcement learning algorithm to allow AidIR to continue learning the dialog policy.

Keywords

• interactive dialog system
• human–computer interaction
• natural language processing
• transfer learning
• reinforcement learning