Acquiring Constraints on Filler-Gap Dependencies from Structural Collocations: Assessing a Computational Learning Model of Island-Insensitivity in Norwegian

Children induce complex syntactic knowledge from their native language input. A long-standing discussion focuses on types of learning biases that help them arrive at correct generalization and solve induction problems posed by impoverished input. Studies employing computational models for learning specific language phenomena serve as testing grounds for evaluating types of biases required for successful acquisition. Recent work by Pearl and Sprouse (2013b) demonstrates that a distributional learner that tracks trigrams over structurally annotated input can acquire wh-filler-gap dependencies and island constraints on them in English. While intriguing, it is unclear yet whether a similar distributional learning model is a viable mechanism for learning island facts in other languages given the possibility of cross-linguistic variation. In this study, we explore whether a distributional learner can acquire wh- and relative clause filler-gap dependencies and island constraints in Norwegian from child-directed annotated text. We find that the proposed learning strategy can capture some patterns of island-insensitivity in Norwegian while failing to learn others due to a lack of relevant data in the input. Our findings suggest that given limited input data, a simple n-gram-based distributional learning over structured representations may not be sufficient to fully recover human-like knowledge of filler-gap dependency relations and island constraints cross-linguistically.