Relative importance in sentence processing

N Hollenstein, L Beinborn - arXiv preprint arXiv:2106.03471, 2021 - arxiv.org
Determining the relative importance of the elements in a sentence is a key factor for
effortless natural language understanding. For human language processing, we can …

[HTML][HTML] Neural networks as cognitive models of the processing of syntactic constraints

S Arehalli, T Linzen - Open Mind, 2024 - direct.mit.edu
Languages are governed by syntactic constraints—structural rules that determine which
sentences are grammatical in the language. In English, one such constraint is subject-verb …

Syntax through rapid synaptic changes

L Sun, SG Manohar - bioRxiv, 2023 - biorxiv.org
Syntax is a central organizing component of human language but few models explain how it
may be implemented in neurons. We combined two rapid synaptic rules to demonstrate how …

STRUCTURAL REPRESENTATIONS IN ONLINE SYNTACTIC PROCESSING: AN ARTIFICIAL NEURAL NETWORK APPROACH

SGR Arehalli - 2023 - jscholarship.library.jhu.edu
Sentences of a language abide by rules called syntactic constraints which govern the form
those sentences may take. Verifying that these constraints are satisfied requires …

Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones?

H Bansal, G Bhatt, S Agarwal - arXiv preprint arXiv:2010.04976, 2020 - arxiv.org
Previous work suggests that RNNs trained on natural language corpora can capture number
agreement well for simple sentences but perform less well when sentences contain …

[PDF][PDF] Analyzing the Learnability and Representability of Recurrent Architectures

P MAINI - 2020 - pratyushmaini.github.io
LSTMs were introduced to mitigate the problem of vanishing gradients in standard recurrent
architectures. Pooling-based recurrent neural architectures consistently outperform their …

[引用][C] Predicting banking stock prices using RNN, LSTM, and GRU approach

D Satria - Applied Computer Science, 2023