Learning perceptually grounded word meanings from unaligned parallel data
In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at traini...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Springer-Verlag
2013
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| Online Access: | http://hdl.handle.net/1721.1/81275 https://orcid.org/0000-0002-8293-0492 |