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 …
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 …
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 …
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 …
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?
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 …
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 …
architectures. Pooling-based recurrent neural architectures consistently outperform their …