Analysing the Multiple Timescale Recurrent Neural Network for Embodied Language Understanding

How the human brain understands natural language and how we can exploit this understanding for building intelligent grounded language systems is open research. Recently, researchers 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 chapter we investigate the characteristics of such an architecture and propose a model based on the Multiple Timescale Recurrent Neural Network, extended by embodied visual perception, and tested in a real world scenario. 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. In addition we rigorously study the timescale mechanism (also known as hysteresis) and explore the impact of the architectural connectivity in the language acquisition task.

@InCollection{HMW15, 
 	 author =  {Heinrich, Stefan and Magg, Sven and Wermter, Stefan},  
 	 title = {Analysing the Multiple Timescale Recurrent Neural Network for Embodied Language Understanding}, 
 	 booktitle = {Artificial Neural Networks -- Methods and Applications in Bio-/Neuroinformatics},
 	 editors = {Petia D. Koprinkova-Hristova and Valeri M. Mladenov and Nikola K. Kasabov},
 	 number = {},
 	 volume = {4},
 	 pages = {149--174},
 	 year = {2015},
 	 month = {Jan},
 	 publisher = {Springer International Publishing Switzerland},
 	 doi = {10.1007/978-3-319-09903-3_8}, 
 }