TY - GEN
T1 - The multimodal correction detection problem
AU - Azaria, Amos
AU - Nivasch, Keren
N1 - Publisher Copyright:
© 2019 International Foundation for Autonomous Agents and Multiagent Systems. All rights reserved.
PY - 2019
Y1 - 2019
N2 - In order for socially aware agents to be truly useful, they should have abilities associated with human intelligence, such as the ability to detect their own mistakes from user reactions. This is an instance of implicit feedback. In this work we address the problem of detecting an agent's mistakes by identifying when the user tries to correct the agent. We refer to this problem as the Correction Detection task. We use a multimodal approach, using both the voice (acoustics and non-verbal sounds) as well as the transcript of the user's spoken commands.
AB - In order for socially aware agents to be truly useful, they should have abilities associated with human intelligence, such as the ability to detect their own mistakes from user reactions. This is an instance of implicit feedback. In this work we address the problem of detecting an agent's mistakes by identifying when the user tries to correct the agent. We refer to this problem as the Correction Detection task. We use a multimodal approach, using both the voice (acoustics and non-verbal sounds) as well as the transcript of the user's spoken commands.
KW - Correction detection
KW - Human-agent interaction
KW - Implicit feedback
KW - Multimodal deep learning architecture
KW - Socially aware personal assistant
UR - https://www.scopus.com/pages/publications/85076929770
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AN - SCOPUS:85076929770
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 1784
EP - 1786
BT - 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
T2 - 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Y2 - 13 May 2019 through 17 May 2019
ER -