Autor: Prickett, Brandon - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Modelling a subregular bias in phonological learning with Recurrent Neural Networks
Autorzy:: Prickett, Brandon
Powiązania:: https://bibliotekanauki.pl/articles/2061408.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
Tematy:: neural networks
learning bias
formal language theory
phonology
Opis:: A number of experiments have demonstrated what seems to be a bias in human phonological learning for patterns that are simpler according to Formal Language Theory (Finley and Badecker 2008; Lai 2015; Avcu 2018). This paper demonstrates that a sequence-to-sequence neural network (Sutskever et al. 2014), which has no such restriction explicitly built into its architecture, can successfully capture this bias. These results suggest that a bias for patterns that are simpler according to Formal Language Theory may not need to be explicitly incorporated into models of phonological learning.
Źródło:: Journal of Language Modelling; 2021, 9, 1; 67--96
2299-856X
2299-8470
Pojawia się w:: Journal of Language Modelling
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Learning reduplication with a neural network that lacks explicit variables
Autorzy:: Prickett, Brandon
Traylor, Aaron
Pater, Joe
Powiązania:: https://bibliotekanauki.pl/articles/24201229.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
Tematy:: neural networks
reduplication
symbolic computation
connectionism
generalization
phonology
Opis:: Reduplicative linguistic patterns have been used as evidence for explicit algebraic variables in models of cognition.1 Here, we show that a variable-free neural network can model these patterns in a way that predicts observed human behavior. Specifically, we successfully simulate the three experiments presented by Marcus et al. (1999), as well as Endress et al.’s (2007) partial replication of one of those experiments. We then explore the model’s ability to generalize reduplicative mappings to different kinds of novel inputs. Using Berent’s (2013) scopes of generalization as a metric, we claim that the model matches the scope of generalization that has been observed in humans. We argue that these results challenge past claims about the necessity of symbolic variables in models of cognition.
Źródło:: Journal of Language Modelling; 2022, 10, 1; 1--38
2299-856X
2299-8470
Pojawia się w:: Journal of Language Modelling
Dostawca treści:: Biblioteka Nauki

Artykuł

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