Abstract
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.
| Original language | English |
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| Title of host publication | 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 |
| Pages | 1784-1786 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781510892002 |
| State | Published - 2019 |
| Event | 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada Duration: 13 May 2019 → 17 May 2019 https://dl.acm.org/doi/proceedings/10.5555/3306127 |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| Volume | 3 |
Conference
| Conference | 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 |
|---|---|
| Country/Territory | Canada |
| City | Montreal |
| Period | 13/05/19 → 17/05/19 |
| Internet address |
Keywords
- Correction detection
- Human-agent interaction
- Implicit feedback
- Multimodal deep learning architecture
- Socially aware personal assistant
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
- Control and Systems Engineering