Reservoir Computing for Robot Language Acquisition

Après un mois d'Août ensoleillé, Bordeaux Machine Learning Meetup reprend son rythme. :-)

Xavier Hinaut (https://sites.google.com/site/xavierhinaut/), un chercheur à l'inria de Bordeaux va nous présenter un type de Réseaux de neurones récurrent utilisé dans la compréhension de la langue cette fois-ci.

Plus de détail est en ci-dessous :

How do children learn language? Could we use robots to model children language acquisition? This question is linked to a more general issue: how does the brain associate sequences of symbols to internal symbolic or sub-symbolic representations? I will present a Recurrent Neural Network (RNN), namely an Echo State Network (ESN) or Reservoir, that performs sentence comprehension and can be used for Human-Robot Interaction. The RNN is trained to map sentence structures to meanings (i.e. predicates). This model has interesting capabilities, for instance it can learn to "understand" French and English at the same time. Moreover, it is flexible and can be trained on different kinds of output predicate representations.

The objective of this model is double: to improve HRI and provide neural models of language acquisition. From the HRI point of view, this model enables one (1) to gain adaptability because the system is trained on corpus examples (no need to predefine a parser for each language), (2) to be able to process natural language sentences instead of stereotypical sentences (i.e. "put cup left"), and (3) to be able to generalize to unknown sentence structures (not in the training data set). From the computational neuroscience and developmental robotics point of view, the aim of this architecture is to model and test hypotheses about child learning processes of language acquisition (Tomasello 2003).