Embodied Language Understanding with a Multiple Timescale Recurrent Neural Network. Heinrich, S., Weber, C., & Wermter, S.
Embodied Language Understanding with a Multiple Timescale Recurrent Neural Network [pdf]Paper  Embodied Language Understanding with a Multiple Timescale Recurrent Neural Network [link]Website  abstract   bibtex   
How the human brain understands natural language and what we can learn for intelligent systems is open research. Recently, re-searchers claimed that language is embodied in most – if not all – sensory and sensorimotor modalities and that the brain's architecture favours the emergence of language. In this paper we investigate the characteris-tics of such an architecture and propose a model based on the Multiple Timescale Recurrent Neural Network, extended by embodied visual per-ception. We show that such an architecture can learn the meaning of utterances with respect to visual perception and that it can produce verbal utterances that correctly describe previously unknown scenes.
@article{
 title = {Embodied Language Understanding with a Multiple Timescale Recurrent Neural Network},
 type = {article},
 keywords = {Embodied Language,Language Acquisition,MTRNN},
 websites = {http://www.informatik.uni-hamburg.de/WTM/},
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 created = {2017-09-01T15:53:51.244Z},
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 last_modified = {2017-09-01T15:53:51.342Z},
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 abstract = {How the human brain understands natural language and what we can learn for intelligent systems is open research. Recently, re-searchers claimed that language is embodied in most – if not all – sensory and sensorimotor modalities and that the brain's architecture favours the emergence of language. In this paper we investigate the characteris-tics of such an architecture and propose a model based on the Multiple Timescale Recurrent Neural Network, extended by embodied visual per-ception. We show that such an architecture can learn the meaning of utterances with respect to visual perception and that it can produce verbal utterances that correctly describe previously unknown scenes.},
 bibtype = {article},
 author = {Heinrich, Stefan and Weber, Cornelius and Wermter, Stefan}
}
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