Publications (12)
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 Paradigm for Situated and Goal-Driven Language Learning
Jon Gauthier, Igor Mordatch
A distinguishing property of human intelligence is the ability to flexibly use language in order to communicate complex ideas with other humans in a variety of contexts. Research i…
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…
Emergent morpho-phonological representations in self-supervised speech models
Jon Gauthier, Canaan Breiss, Matthew Leonard +1
Self-supervised speech models can be trained to efficiently recognize spoken words in naturalistic, noisy environments. However, we do not understand the types of linguistic repres…
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…
Language model acceptability judgements are not always robust to context
Koustuv Sinha, Jon Gauthier, Aaron Mueller +4
Targeted syntactic evaluations of language models ask whether models show stable preferences for syntactically acceptable content over minimal-pair unacceptable inputs. Most target…
A Fast Unified Model for Parsing and Sentence Understanding
Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi +3
Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences. However, they suffer from two key technical problems that…
The neural dynamics of auditory word recognition and integration
Jon Gauthier, Roger Levy
Listeners recognize and integrate words in rapid and noisy everyday speech by combining expectations about upcoming content with incremental sensory evidence. We present a computat…
Probing self-supervised speech models for phonetic and phonemic information: a case study in aspiration
Kinan Martin, Jon Gauthier, Canaan Breiss +1
Textless self-supervised speech models have grown in capabilities in recent years, but the nature of the linguistic information they encode has not yet been thoroughly examined. We…
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…
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…