Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.

Monotone Quantifiers Emerge via Iterated Learning / Carcassi, Fausto; Steinert-Threlkeld, Shane; Szymanik, Jakub. - In: COGNITIVE SCIENCE. - ISSN 0364-0213. - 45:8(2021), pp. e1302701-e1302730. [10.1111/cogs.13027]

Monotone Quantifiers Emerge via Iterated Learning

Szymanik, Jakub
2021-01-01

Abstract

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.
2021
8
Carcassi, Fausto; Steinert-Threlkeld, Shane; Szymanik, Jakub
Monotone Quantifiers Emerge via Iterated Learning / Carcassi, Fausto; Steinert-Threlkeld, Shane; Szymanik, Jakub. - In: COGNITIVE SCIENCE. - ISSN 0364-0213. - 45:8(2021), pp. e1302701-e1302730. [10.1111/cogs.13027]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/364227
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