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Experiments with Generative Models for Dependency Tree Linearization.

, and . EMNLP, page 1978-1983. The Association for Computational Linguistics, (2015)

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The 'universal' structure of name grammars and the impact of social engineering on the evolution of natural information systems., , , , , , and . CogSci, cognitivesciencesociety.org, (2013)Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations., , and . CoRR, (2019)Estimating Predictive Rate-Distortion Curves via Neural Variational Inference., and . Entropy, 21 (7): 640 (2019)Experiments with Generative Models for Dependency Tree Linearization., and . EMNLP, page 1978-1983. The Association for Computational Linguistics, (2015)The Social Evolution and Communicative Function of Noun Classification., , , and . CogSci, cognitivesciencesociety.org, (2015)RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency., , , and . CoRR, (2018)Grammatical cues are largely, but not completely, redundant with word meanings in natural language., , , , and . CoRR, (2022)Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT., , , and . EACL, page 2522-2532. Association for Computational Linguistics, (2021)An Information-Theoretic Explanation of Adjective Ordering Preferences., , , , and . CogSci, cognitivesciencesociety.org, (2018)Neural language models as psycholinguistic subjects: Representations of syntactic state., , , , , and . NAACL-HLT (1), page 32-42. Association for Computational Linguistics, (2019)