@inproceedings{sheng-etal-2023-dialogue,
    title = "A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge",
    author = "Sheng, Zhecheng  and
      Finzel, Raymond  and
      Lucke, Michael  and
      Dufresne, Sheena  and
      Gini, Maria  and
      Pakhomov, Serguei",
    editor = "Muresan, Smaranda  and
      Chen, Vivian  and
      Casey, Kennington  and
      David, Vandyke  and
      Nina, Dethlefs  and
      Koji, Inoue  and
      Erik, Ekstedt  and
      Stefan, Ultes",
    booktitle = "Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.dialdoc-1.8",
    doi = "10.18653/v1/2023.dialdoc-1.8",
    pages = "68--79",
    abstract = "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.",
}

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.
Sheng, Zhecheng Finzel, Raymond Lucke, Michael Dufresne, Sheena Gini, Maria Pakhomov, Serguei
A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge