TY - GEN
T1 - A Database of Multimodal Data to Construct a Simulated Dialogue Partner with Varying Degrees of Cognitive Health
AU - Pan, Ruihao
AU - Liu, Ziming
AU - Yuan, Fengpei
AU - Zare, Maryam
AU - Zhao, Xiaopeng
AU - Passonneau, Rebecca J.
N1 - Funding Information:
This work was partly funded by the National Institute on Aging under the grant number R01AG077003. The experimental protocol was approved by the Internal Review Board at UTK under the number: UTK IRB-21-06631-XM.”
Publisher Copyright:
© European Language Resources Association (ELRA)
PY - 2022
Y1 - 2022
N2 - An assistive robot that could communicate with dementia patients would have great social benefit. An assistive robot Pepper has been designed to administer Referential Communication Tasks (RCTs) to human subjects without dementia as a step towards an agent to administer RCTs to dementia patients, potentially for earlier diagnosis. Currently, Pepper follows a rigid RCT script, which affects the user experience. We aim to replace Pepper’s RCT script with a dialogue management approach, to generate more natural interactions with RCT subjects. A Partially Observable Markov Decision Process (POMDP) dialogue policy will be trained using reinforcement learning, using simulated dialogue partners. This paper describes two RCT datasets and a methodology for their use in creating a database that the simulators can access for training the POMDP policies.
AB - An assistive robot that could communicate with dementia patients would have great social benefit. An assistive robot Pepper has been designed to administer Referential Communication Tasks (RCTs) to human subjects without dementia as a step towards an agent to administer RCTs to dementia patients, potentially for earlier diagnosis. Currently, Pepper follows a rigid RCT script, which affects the user experience. We aim to replace Pepper’s RCT script with a dialogue management approach, to generate more natural interactions with RCT subjects. A Partially Observable Markov Decision Process (POMDP) dialogue policy will be trained using reinforcement learning, using simulated dialogue partners. This paper describes two RCT datasets and a methodology for their use in creating a database that the simulators can access for training the POMDP policies.
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M3 - Conference contribution
AN - SCOPUS:85145880072
T3 - Proceedings - 4th RaPID Workshop: Resources and Processing of Linguistic, Para-Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric/Developmental Impairments, as part of the 13th Edition of the Language Resources and Evaluation Conference, LREC 2022
SP - 86
EP - 93
BT - Proceedings - 4th RaPID Workshop
A2 - Kokkinakis, Dimitrios
A2 - Themistocleous, Charalambos K.
A2 - Fors, Kristina Lundholm
A2 - Tsanas, Athanasios
A2 - Fraser, Kathleen C.
PB - European Language Resources Association (ELRA)
T2 - 4th RaPID Workshop: Resources and Processing of Linguistic, Para-Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric/Developmental Impairments, RAPID 2022
Y2 - 25 June 2022
ER -