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Found 7 papers, 1 papers with code
Modelling Compositionality and Structure Dependence in Natural Language
Drawing on linguistics and set theory, a formalisation of these ideas is presented in the first half of this thesis.
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?
The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read.
A Theory of Relation Learning and Cross-domain Generalization
People readily generalize knowledge to novel domains and stimuli.
The relational processing limits of classic and contemporary neural network models of language processing
In the present study we tested the Story Gestalt model (St. John, 1992), a classic PDP model of text comprehension, and a Sequence-to-Sequence with Attention model (Bahdanau et al., 2015), a contemporary deep learning architecture for text processing.
Predicate learning in neural systems: Discovering latent generative structures
Humans learn complex latent structures from their environments (e. g., natural language, mathematics, music, social hierarchies).
Human-like generalization in a machine through predicate learning
Humans readily generalize, applying prior knowledge to novel situations and stimuli.
