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- W2593271325 abstract "A recurrent neural network model of phonological pattern learning is proposed. The model is a relatively simple neural network with one recurrent layer, and displays biases in learning that mimic observed biases in human learning. Single-feature patterns are learned faster than two-feature patterns, and vowel or consonant-only patterns are learned faster than patterns involving vowels and consonants, mimicking the results of laboratory learning experiments. In non-recurrent models, capturing these biases requires the use of alpha features or some other representation of repeated features, but with a recurrent neural network, these elaborations are not necessary." @default.
- W2593271325 created "2017-03-16" @default.
- W2593271325 creator A5005368705 @default.
- W2593271325 date "2017-01-01" @default.
- W2593271325 modified "2023-10-14" @default.
- W2593271325 title "Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation" @default.
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- W2593271325 doi "https://doi.org/10.18653/v1/w17-0705" @default.
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