Building blocks of a task-oriented dialogue system in the healthcare domain (2021)

Abstract: There has been significant progress in dialogue systems research. However, dialogue systems research in the healthcare domain is still in its infancy. In this paper, we analyse recent studies and outline three building blocks of a task-oriented dialogue system in the healthcare domain: i) privacy-preserving data collection; ii) medical knowledge-grounded dialogue management; and iii) human-centric evaluations. To this end, we propose a framework for developing a dialogue system and show preliminary results of simulated dialogue data generation by utilising expert knowledge and crowd-sourcing.

## introduction there has been significant progress in the research field of the dialogue system in past years with the help of large-scale pre-trained language models (lms) @xcite @xcite . pre-trained lms show a good generalised ability obtained from massive training data collected from the internet and achieve stateof-the-art performance over a wide range of dialogue domains @xcite . while many studies exist on general purpose dialogues, the research on dialogue systems for healthcare applications is still in its infancy. there are two major directions in the development of a dialogue system. one direction is to build a chatbot that can have a conversation with a user. this approach mainly focuses on generating appropriate response given user input and dialogue history. researchers have been working on this direction to create systems to produce more human-like @xcite , consistent @xcite , and empathetic @xcite responses. the other direction is to build a task-oriented dialogue system that performs a specific task, such as triage or diagnosis within the healthcare domain where researchers focus on developing systems that can detect implicit symptoms or make precise diagnosis/triage result @xcite @xcite . in this study, we consider a dialogue system for a sleep coaching programme for healthy people who would like to optimise their sleep. motivated by cognitive behaviour therapy for insomnia (cbt-i), we focus on investigating the relationship between how people think, behave, and sleep @xcite . the first step of the coaching programme is a complaints assessment to identify sleep issues and their potential causes and decide the next step (e.g., referring to sleep apnea treatment, providing a sleep education, suggesting a behaviour change programme, etc). during this process, a coaching provider (coach) plays as an active listener, asking questions to probe specific information, while a coaching receiver (user) has more chance to provide complaints and elaborate on these. real challenges in the development of a dialogue system, especially a machine learning-based system, come from three fundamental questions: i) how to obtain relevant data; ii) how to develop an automated system; and iii) how to evaluate a system. in this paper, we first analyse existing approaches that address the above questions (section 2). then we propose our method to address these questions (section 3) and show preliminary results and discuss its limitations (section 4). the major contributions of this paper are as follows: ## building blocks here we outline three building blocks of a dialogue system in the healthcare domain and identify open research questions for each building block. to this end, we propose a framework for developing a conversation agent for healthcare-related dialogues. ## preliminary results this section describes an initial approach of generating simulated dialogues based on a template and crowdsourced data. the goal of a dialogue is to assess user complaints related to their sleep and identify all potential behavioural factors that might be associated with the reported complaints. ## conclusion in this paper, we analyse recent studies on the development of a dialogue system in the healthcare domain and outline three building blocks, namely: i) privacy-preserving data collection; ii) medical knowledge-grounded dialogue management; and iii) human-centred evaluations. to this end, we propose a framework for developing a dialogue system and show preliminary results of simulated dialogue data generation by utilising expert knowledge and crowdsourcing. in the future study, we foresee working on implementing a user simulator that can interact with a reinforcement learning agent, accessing the quality of the simulated dialogues, and deploying the reinforcement learning agent to interact with both a user simulator and real-world users. liu, and william b dolan. 2020. dialogpt: largescale generative pre-training for conversational response generation. in proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations, pages 270-278.