People learn language variation through exposure to linguistic interactions. The way we take part in these interactions is shaped by our lexical representations, the mechanisms of language processing, and the social context. Existing work has looked at how we learn and store variation in the ambient language. How this is mediated by the social…

Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as…

The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of typologically distant languages. Different sequence-processing neural networks are trained in a set…