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1
Are people still smarter than machines? If so, why? ...
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2
Using Recurrent Neural Networks to Understand Human Reward Learning ...
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3
Selection, Engagement, & Enhancement: A Framework for Modeling Visual Attention ...
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4
Compositional processing emerges in neural networks solving math problems ...
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5
Using playback to investigate multimodal signalling of attractiveness in ring doves (Streptopelia risoria) ...
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6
A Layered Bridge from Sound to Meaning: Investigating Cross-linguistic Phonosemantic Correspondences ...
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A Layered Bridge from Sound to Meaning: Investigating Cross-linguistic Phonosemantic Correspondences ...
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Speechless Reader Model: A neurocognitive model for human reading reveals cognitive underpinnings of baboon lexical decision behavior. ...
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Cross-language structural priming in recurrent neural network language models ...
Abstract: Recurrent neural network (RNN) language models that are trained on large text corpora have shown a remarkable ability to capture properties of human syntactic processing (Linzen & Baroni, 2021). For example, the fact that these models display human-like structural priming effects (Prasad, Van Schijndel, & Linzen, 2019; van Schijndel & Linzen, 2018) suggests that they develop implicit syntactic representations that may not be unlike those of the human language system. A rarely explored question is whether RNNs are also able to simulate aspects of human multilingual sentence processing (Frank, 2021) even though training RNNs on two or more languages simultaneously is technically unproblematic. ...
Keyword: Cognitive Linguistics; Cognitive Neuroscience; Cognitive Science; Computational Neuroscience; Neural Network; Semiotics
URL: https://dx.doi.org/10.48448/naa0-pj67
https://underline.io/lecture/26864-cross-language-structural-priming-in-recurrent-neural-network-language-models
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Cross-language structural priming in recurrent neural network language models ...
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