@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge
%A Sheng, Zhecheng
%A Finzel, Raymond
%A Lucke, Michael
%A Dufresne, Sheena
%A Gini, Maria
%A Pakhomov, Serguei
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sheng-etal-2023-dialogue
%X 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.
%R 10.18653/v1/2023.dialdoc-1.8
%U https://aclanthology.org/2023.dialdoc-1.8
%U https://doi.org/10.18653/v1/2023.dialdoc-1.8
%P 68-79
Markdown (Informal)
[A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge](https://aclanthology.org/2023.dialdoc-1.8) (Sheng et al., dialdoc 2023)
ACL