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20162021
most citedWhat Syntactic Structures block Dependencies in RNN Language Models?

14 citations · 34 across the 8 of their papers we have counts for

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cs.CL20212 cited

Scalable pragmatic communication via self-supervision

Jennifer Hu, Roger Levy, Noga Zaslavsky

Models of context-sensitive communication often use the Rational Speech Act framework (RSA; Frank & Goodman, 2012), which formulates listeners and speakers in a cooperative reasoni…

cs.CL2019

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…

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…

cs.CL2019

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…

cs.CL201914 cited

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…

cs.CL20191 cited

Availability-Based Production Predicts Speakers' Real-time Choices of Mandarin Classifiers

Meilin Zhan, Roger Levy

Speakers often face choices as to how to structure their intended message into an utterance. Here we investigate the influence of contextual predictability on the encoding of lingu…