Proceedings of the Annual Meeting of the Cognitive Science Society

How do learners acquire languages from the limited data avail-able to them? This process must involve some inductivebiases—factors that affect how a learner generalizes—but it isunclear which inductive biases can explain observed patternsin language acquisition. To facilitate computational model-ing aimed at addressing this question, we introduce a frame-work for giving particular linguistic inductive biases to a neu-ral network model; such a model can then be used to em-pirically explore the effects of those inductive biases. Thisframework disentangles universal inductive biases, which areencoded in the initial values of a neural network’s param-eters, from non-universal factors, which the neural networkmust learn from data in a given language. The initial statethat encodes the inductive biases is found with meta-learning,a technique through which a model discovers how to acquirenew languages more easily via exposure to many possible lan-guages. By controlling the properties of the languages that areused during meta-learning, we can control the inductive biasesthat meta-learning imparts. We demonstrate this frameworkwith a case study based on syllable structure. First, we specifythe inductive biases that we intend to give our model, and thenwe translate those inductive biases into a space of languagesfrom which a model can meta-learn. Finally, using existinganalysis techniques, we verify that our approach has impartedthe linguistic inductive biases that it was intended to impart.