A Connectionist Model for Acquisition of Syntactic Islands

This paper addresses learning biases for language acquisition in a computational modeling approach for the task of learning complex syntactic phenomena. Children have learning biases for acquisition of their language. Many generative linguists have argued that children have at least an innate, domain-specific bias (i.e., “Universal Grammar”(UG) hypothesis). This controversial hypothesis has been supported by studies on language acquisition and complex language phenomena, such as rules on long- distance wh-dependencies, the so-called “Syntactic islands”. Some researchers have proposed probability-based computational models that successfully learn syntactic islands. However, these models assume implausible biases. To overcome this problem, we propose a connectionist model using Jordan's recurrent network and demonstrate successful acquisition of syntactic islands by this model, under a developmental processing limitation. Our model not only learns syntactic islands, but also simply assumes more plausible and developmentally realistic biases than the probability-based models. These results suggest that the developmental processing limitation in the early period is necessary for acquisition of syntactic islands.