activity
20182022
most citedOn the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior

110 citations · 169 across the 8 of their papers we have counts for

collaborators

12 papers

cs.CL20222 cited

Exhaustivity and anti-exhaustivity in the RSA framework: Testing the effect of prior beliefs

Alexandre Cremers, Ethan G. Wilcox, Benjamin Spector

During communication, the interpretation of utterances is sensitive to a listener's probabilistic prior beliefs, something which is captured by one currently influential model of p…

cs.CL2020

Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization

Tristan Thrush, Ethan Wilcox, Roger Levy

Previous studies investigating the syntactic abilities of deep learning models have not targeted the relationship between the strength of the grammatical generalization and the amo…

cs.CL2020

Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models

Ethan Wilcox, Peng Qian, Richard Futrell +3

Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We asse…

cs.CL2020110 cited

On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior

Ethan Gotlieb Wilcox, Jon Gauthier, Jennifer Hu +2

Human reading behavior is tuned to the statistics of natural language: the time it takes human subjects to read a word can be predicted from estimates of the word's probability in…

cs.CL202027 cited

A Systematic Assessment of Syntactic Generalization in Neural Language Models

Jennifer Hu, Jon Gauthier, Peng Qian +2

While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage p…

cs.CL2019

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…