A Dialogue System for Assessing Activities of Daily Living: Improving
  Consistency with Grounded Knowledge
Zhecheng Sheng Raymond Finzel Michael Lucke Sheena Dufresne Maria Gini Serguei Pakhomov
In healthcare, the ability to care for oneself is reflected in the
"Activities of Daily Living (ADL)," which serve as a measure of functional
ability (functioning). A lack of functioning may lead to poor living conditions
requiring personal care and assistance. To accurately identify those in need of
support, assistance programs continuously evaluate participants' functioning
across various domains. However, the assessment process may encounter
consistency issues when multiple assessors with varying levels of expertise are
involved. Novice assessors, in particular, may lack the necessary preparation
for real-world interactions with participants. To address this issue, we
developed a dialogue system that simulates interactions between assessors and
individuals of varying functioning in a natural and reproducible way. The
dialogue system consists of two major modules, one for natural language
understanding (NLU) and one for natural language generation (NLG),
respectively. In order to generate responses consistent with the underlying
knowledge base, the dialogue system requires both an understanding of the
user's query and of biographical details of an individual being simulated. To
fulfill this requirement, we experimented with query classification and
generated responses based on those biographical details using some recently
released InstructGPT-like models.