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Dependency locality as an explanatory principle for word order
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In: Prof. Levy (2022)
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A Systematic Assessment of Syntactic Generalization in Neural Language Models
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In: Association for Computational Linguistics (2021)
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Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
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In: Association for Computational Linguistics (2021)
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Cognitive Science Honors the Memory of Jeffrey Elman
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In: MIT Press (2021)
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SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
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In: Association for Computational Linguistics (2021)
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Neural language models as psycholinguistic subjects: Representations of syntactic state
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In: Association for Computational Linguistics (2021)
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Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
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In: Association for Computational Linguistics (2021)
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Maze Made Easy: Better and easier measurement of incremental processing difficulty
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In: Other repository (2021)
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Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication ...
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Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication.
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Do domain-general executive resources play a role in linguistic prediction? Re-evaluation of the evidence and a path forward
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In: Prof. Fedorenko (2021)
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Pronoun interpretation in Mandarin Chinese follows principles of Bayesian inference
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In: PLoS (2021)
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Assessing Language Proficiency from Eye Movements in Reading
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In: Association for Computational Linguistics (2021)
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Implicit Gender Bias in Linguistic Descriptions for Expected Events: The Cases of the 2016 United States and 2017 United Kingdom Elections
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In: Sage (2021)
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Language Learning and Processing in People and Machines
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In: Association for Computational Linguistics (2021)
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Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing
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In: Wiley (2021)
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Abstract:
Copyright © 2020 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society (CSS) A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation-based and memory-based. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. Our model, lossy-context surprisal, holds that the processing difficulty at a word in context is proportional to the surprisal of the word given a lossy memory representation of the context—that is, a memory representation that does not contain complete information about previous words. We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language-dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory-based theories, can be derived from lossy-context surprisal as a special case of a novel, more general principle called information locality.
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URL: https://hdl.handle.net/1721.1/135946
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A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans ...
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A Rate–Distortion view of human pragmatic reasoning
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Implicit Gender Bias in Linguistic Descriptions for Expected Events: The Cases of the 2016 United States and 2017 United Kingdom Elections ...
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