ECHOROB

Echo State Networks for Developing Language Robots

Coordinatore	UNIVERSITAET HAMBURG    more  Organization address address: EDMUND-SIEMERS-ALLEE 1 city: HAMBURG postcode: 20146 contact info Titolo: Ms. Nome: Linda Cognome: Reams-Behboud Email: send email Telefono: +49 40 42838 4425 Fax: +49 40 427977 371
Nazionalità Coordinatore	Germany [DE]
Totale costo	161˙968 €
EC contributo	161˙968 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2013-IEF
Funding Scheme	MC-IEF
Anno di inizio	2015
Periodo (anno-mese-giorno)	2015-03-01   -   2017-02-28

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITAET HAMBURG  Organization address address: EDMUND-SIEMERS-ALLEE 1 city: HAMBURG postcode: 20146 contact info Titolo: Ms. Nome: Linda Cognome: Reams-Behboud Email: send email Telefono: +49 40 42838 4425 Fax: +49 40 427977 371	DE (HAMBURG)	coordinator	161˙968.80

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 Obiettivo del progetto (Objective)

'In this project, we propose to build a biologically plausible model of sentence comprehension based on recurrent neural networks called reservoirs. Human sentence comprehension is mainly handled by prefrontal cortex areas, which have highly recurrent connectivity. In both biological and artificial neural networks this recurrence is supposed to enable the management of different aspects of time such as working memory and contextual information processing. Here, we propose to develop the Reservoir Computing (RC) paradigm – in particular Echo State Networks (ESN) with incremental learning – to model language comprehension at the sentence level given sequential inputs of words or phonemes. Based on our initial research, a model processing syntactic sentence structures was able to demonstrate generalisation and online prediction capabilities while processing sequential input. For less frequent inputs, the model provided potential explanation for human electrophysiological data. Building on this research, a new model is proposed with the following objectives: (1) processing of all semantic information enabling contextual processing and richer representation of meaning, (2) implementing incremental learning with noisy supervision enabling realistic developmental language acquisition from simple to complex sentences, (3) demonstrating that the model can learn from naïve user’s utterances in several languages, and (4) demonstrating the ability of this model when embodied in a robot to acquire extended language capabilities through human-robot interaction accounting for developmental schemes.'