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The emergence of monotone quantifiers via iterated learning
| Content Provider | Scilit |
|---|---|
| Author | Carcassi, Fausto Steinert-Threlkeld, Shane Szymanik, Jakub |
| Copyright Year | 2019 |
| Description | Natural languages exhibit many \emph{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. |
| DOI | 10.31234/osf.io/8swtd |
| Language | English |
| Publisher | Center for Open Science |
| Publisher Date | 2019-05-09 |
| Access Restriction | Open |
| Subject Keyword | History and Philosophy of Science Iterated Learning Monotone Quantifiers |
| Content Type | Text |
| Resource Type | Article |
