In previous research a model for thematic role assignment (θRARes) was proposed, using the Reservoir Computing paradigm. This language comprehension model consisted of a recurrent neural network (RNN) with fixed random connections which models distributed processing in the prefrontal cortex, and an output layer which models the striatum. In contrast to this previous batch learning method, in this paper we explored a more biological learning mechanism. A new version of the model (i-θRARes) was developed that permitted incremental learning, at each time step. Learning was based on a stochastic gradient descent method. We report here results showing that this incremental version was successfully able to learn a corpus of complex grammatical constructions, reinforcing the neurocognitive plausibility of the model from a language acquisition perspective. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Hinaut, X., & Wermter, S. (2014). An incremental approach to language acquisition: Thematic role assignment with echo state networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 33–40). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_5
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