The emergence of monotone quantifiers via iterated learning

Fausto Carcassi; Shane Steinert-Threlkeld; Jakub Szymanik

doi:10.17605/OSF.IO/UME39

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The emergence of monotone quantifiers via iterated learning

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Fausto Carcassi
Shane Steinert-Threlkeld
Jakub Szymanik

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Description: This project explores the evolution of the universal of monotonicity in the semantics of quantifiers with the help of a computational model. It combines the iterated learning paradigm with neural networks as a model of learning. This project contains the data that we present in the paper. The code for reproducing the results is available at https://github.com/thelogicalgrammar/NeuralNetIteratedQuantifiers.

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