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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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A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans ...
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Abstract:
We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena. Human reaction time data comes from a novel online experimental paradigm called the Interpolated Maze task. We compare human reaction times to by-word probabilities for four contemporary language models, with different architectures and trained on a range of data set sizes. We find that across many phenomena, both humans and language models show increased processing difficulty in ungrammatical sentence regions with human and model `accuracy' scores (a la Marvin and Linzen(2018)) about equal. However, although language model outputs match humans in direction, we show that models systematically under-predict the difference in magnitude of incremental processing difficulty between grammatical and ungrammatical sentences. Specifically, when models encounter syntactic violations they ... : To appear at ACL 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2106.03232 https://dx.doi.org/10.48550/arxiv.2106.03232
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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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