110 citations · 242 across the 15 of their papers we have counts for
5 papers · 1 filter
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
Aixiu An, Peng Qian, Ethan Wilcox +1
Neural language models have achieved state-of-the-art performances on many NLP tasks, and recently have been shown to learn a number of hierarchically-sensitive syntactic dependenc…
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
Ethan Wilcox, Roger Levy, Richard Futrell
Deep learning sequence models have led to a marked increase in performance for a range of Natural Language Processing tasks, but it remains an open question whether they are able t…
What Syntactic Structures block Dependencies in RNN Language Models?
Ethan Wilcox, Roger Levy, Richard Futrell
Recurrent Neural Networks (RNNs) trained on a language modeling task have been shown to acquire a number of non-local grammatical dependencies with some success. Here, we provide n…
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian, Richard Futrell +2
State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical…
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
Richard Futrell, Ethan Wilcox, Takashi Morita +3
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental repres…