Are Children 'Lazy Learners'? A Comparison of Natural and Machine Learning of Stress

Do children acquire rules for main stress assignment or do they learn stress in an exemplar-based way? In the language acquisition literature, the former approach has been advocated without exception: although they hear most words produced with their appropriate stress pattern, children are taken to extract rules and do not store stress patterns lexically. The evidence for a rule-based approach is investigated and it will be argued that in the literature this approach is preferred due to an extremely simplified interpretation of exemplar-based models. We will report experiments showing that Instance-Based Learning, an exemplar-based model, makes the same kinds of stress related errors in production that children make: (i) the amount of production errors is related to metrical markedness, and (ii) stress shifts and errors with respect to the segmental and syllabic structure of words typically take the form of a regularization of stress patterns. InstanceBased Learning belongs to a class of Lazy Learning algorithms. In these algorithms, no explicit abstractions in the form of decision trees or rules are derived; abstraction is driven by similarity during performance.ion is driven by similarity during performance. Our results indicate that at least for this domain, this kind of lazy learning is a valid alternative to rule-based learning. Moreover the results plead for a reanalysis of language acquisition data in terms of exemplar-based