Intent-context fusioning in healthcare dialogue-based systems using JDL model

Abstract

A revolutionized wave of intelligent assistants has emerged in daily life of human over the recent years, therefore huge progress has been witnessed for development of healthcare assistants having the capability to communicate with users. However, the conversational complexities demand building more personalized and user-oriented dialogue process systems. To support human-computer dialogue process many models have been proposed. Considering personalization aspect, this research work presents novel Context-aware Dialogue Manager (CADM) model with its foundation based on well-known JDL fusion model. The proposed model addresses modern techniques for multi-turn dialogue process, by identifying dialogue intents, contexts and fusing personalized contexts over them. The model also maintains the dialogue context for progressing complex and multi-turn dialogue. It also helps using intent-context relationship in identifying optimized knowledge source for accurate dialogue expansion and its coherence. CADM functionality is discussed using support of Intelligent Medical Assistant in healthcare domain, which has the speech-based capability to communicate with users.