CLASP
The Centre for Linguistic Theory and Studies in Probability

Learning (a language) to Communicate Efficiently

Although languages vary enormously, there are nevertheless universal tendencies in word meanings, such that similar or identical meanings often appear in unrelated languages. A major question is how to account for such semantic universals and variation of the lexicon in a principled and unified way. An influential approach to this question proposes that word meanings may reflect adaptation to pressure for efficient communication – this principle holds that languages are under pressure to be simultaneously informative (so as to support effective communication) and simple (so as to minimize cognitive load). We offer computational support for this principle in the domain of color words i.e, how languages partition the semantic space of colours by linguistic terms. Our framework uses reinforcement learning for automated agents to generate partitions that are efficient and consistent with those found in many languages in the World Colour Survey. We argue that our framework provides a flexible and powerful tool to address similar fundamental questions about universals in other domains as well.