Conversational Agent for Daily Living Assessment Coaching (CADLAC) is a multi-modal conversational agent system designed to impersonate “individuals” with various levels of ability in activities of daily living (ADLs: e.g., dressing, bathing, mobility, etc.) for use in training professional assessors how to conduct interviews to determine one's level of functioning. The system is implemented on the MindMeld platform for conversational AI and features a Bidirectional Long Short-Term Memory topic tracker that allows the agent to navigate conversations spanning 18 different ADL domains, a dialogue manager that interfaces with a database of over 10,000 historical ADL assessments, a rule-based Natural Language Generation (NLG) module, and a pre-trained open-domain conversational sub-agent (based on GPT-2) for handling conversation turns outside of the 18 ADL domains. CADLAC is delivered via state-of-the-art web frameworks to handle multiple conversations and users simultaneously and is enabled with voice interface. The paper includes a description of the system design and evaluation of individual components followed by a brief discussion of current limitations and next steps.
|Original language||English (US)|
|Title of host publication||EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the System Demonstrations|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||8|
|State||Published - 2021|
|Event||16th Conference of the European Chapter of the Associationfor Computational Linguistics: System Demonstrations, EACL 2021 - Virtual, Online|
Duration: Apr 19 2021 → Apr 23 2021
|Name||EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the System Demonstrations|
|Conference||16th Conference of the European Chapter of the Associationfor Computational Linguistics: System Demonstrations, EACL 2021|
|Period||4/19/21 → 4/23/21|
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. We would also like to thank Pamela Miller, Sidney Kiltie, and Elise Moore for help with transforming certified assessor notes to natural language format and Julia Garbuz for helping to develop and conduct the surveys of DHS assessors.
© 2021 Association for Computational Linguistics