27 citations · 27 across the 5 of their papers we have counts for
7 papers · 1 filter
Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNN
Kai Schenck, Gašper Beguš
This paper outlines the methodology for modeling tonal learning in fully unsupervised models of human language acquisition. Tonal patterns are among the computationally most comple…
Exploring the encoding of linguistic representations in the Fully-Connected Layer of generative CNNs for Speech
Bruno Ferenc Šegedin, Gasper Beguš
Interpretability work on the convolutional layers of CNNs has primarily focused on computer vision, but some studies also explore correspondences between the latent space and the o…
Large language models and (non-)linguistic recursion
Maksymilian Dąbkowski, Gašper Beguš
Recursion is one of the hallmarks of human language. While many design features of language have been shown to exist in animal communication systems, recursion has not. Previous re…
Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
Gašper Beguš, Thomas Lu, Zili Wang
Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a…
Large Linguistic Models: Investigating LLMs' metalinguistic abilities
Gašper Beguš, Maksymilian Dąbkowski, Ryan Rhodes
The performance of large language models (LLMs) has recently improved to the point where models can perform well on many language tasks. We show here that--for the first time--the…
Deep Sound Change: Deep and Iterative Learning, Convolutional Neural Networks, and Language Change
Gašper Beguš
This paper proposes a framework for modeling sound change that combines deep learning and iterative learning. Acquisition and transmission of speech is modeled by training generati…