Modelling child comprehension: A case of suffixal passive construction in Korean

The present study investigates a computational model's ability to capture monolingual children's language behaviour during comprehension in Korean, an understudied language in the field. Specifically, we test whether and how two neural network architectures (LSTM, GPT-2) cope with a suffixal passive construction involving verbal morphology and required interpretive procedures (i.e., revising the mapping between thematic roles and case markers) driven by that morphology. To this end, we fine-tune our models via patching (i.e., pre-trained model + caregiver input) and hyperparameter adjustments, and measure their binary classification performance on the test sentences used in a behavioural study manifesting scrambling and omission of sentential components to varying degrees. We find that, while these models' performance converges with the children's response patterns found in the behavioural study to some extent, the models do not faithfully simulate the children's comprehension behaviour pertaining to the suffixal passive, yielding by-model, by-condition, and by-hyperparameter asymmetries. This points to the limits of the neural networks' capacity to address child language features. The implications of this study invite subsequent inquiries on the extent to which computational models reveal developmental trajectories of child language that have been unveiled through corpus-based or experimental research.