We present preliminary work-in-progress results of a project focused on developing a conversational agent system to help with training certified assessors in conducting assessments of functioning in activities of daily living. To date, we have designed a modular task-based conversational agent system and collected hypothetical dialogue data required for training system components as well as a knowledge base needed to generate a wide variety of synthetic profiles of "individuals" being assessed. One of the key components of the system is the topic tracking module that determines the current topic of the conversation. We report the results of experiments with several machine learning approaches to topic/domain classification. The highest accuracy of 83% was achieved with a bidirectional long short-term memory (BiLSTM) model with pre-trained GloVe embeddings. In addition to these results, we also discuss some of the other challenges that we have encountered so far and potential solutions that we are currently pursuing.
|Original language||English (US)|
|Number of pages||6|
|Journal||CEUR Workshop Proceedings|
|State||Published - 2020|
|Event||1st Workshop on Artificial Intelligence for Function, Disability, and Health, AI4Function 2020 - Virtual, Online|
Duration: Jan 7 2021 → Jan 8 2021
Bibliographical noteFunding Information:
The work on this project was supported by funding from the Minnesota Department of Human Services. We would like to thank the people at DSD and MNIT for help with project specifications, gathering of historical data, and expert guidance on domain-specific aspects of the project.
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).