110 citations · 152 across the 4 of their papers we have counts for
6 papers
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…
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…
Linking artificial and human neural representations of language
Jon Gauthier, Roger Levy
What information from an act of sentence understanding is robustly represented in the human brain? We investigate this question by comparing sentence encoding models on a brain dec…
Does the brain represent words? An evaluation of brain decoding studies of language understanding
Jon Gauthier, Anna Ivanova
Language decoding studies have identified word representations which can be used to predict brain activity in response to novel words and sentences (Anderson et al., 2016; Pereira…
Word learning and the acquisition of syntactic--semantic overhypotheses
Jon Gauthier, Roger Levy, Joshua B. Tenenbaum
Children learning their first language face multiple problems of induction: how to learn the meanings of words, and how to build meaningful phrases from those words according to sy…
Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning
Li Lucy, Jon Gauthier
Distributional word representation methods exploit word co-occurrences to build compact vector encodings of words. While these representations enjoy widespread use in modern natura…