The emergence of monotone quantifiers via iterated learning
- Fausto Carcassi, Centre for Language Evolution, University of Edinburgh, Edinburgh, United Kingdom
- Shane Steinert-Threlkeld, Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands
- Jakub Szymanik, Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands
AbstractNatural languages exhibit many semantic universals: properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal: that all simple determiners denote monotone quantifiers. While existing work has shown that monotone quantifiers are easier to learn, we provide a complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, in an iterated learning paradigm, with neural networks as agents, monotone quantifiers regularly evolve.
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