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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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| 2 |
A Systematic Assessment of Syntactic Generalization in Neural Language Models
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In: Association for Computational Linguistics (2021)
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| 3 |
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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| 4 |
Cognitive Science Honors the Memory of Jeffrey Elman
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In: MIT Press (2021)
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| 5 |
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
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In: Association for Computational Linguistics (2021)
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| 6 |
Neural language models as psycholinguistic subjects: Representations of syntactic state
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In: Association for Computational Linguistics (2021)
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| 7 |
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
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In: Association for Computational Linguistics (2021)
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| 8 |
Maze Made Easy: Better and easier measurement of incremental processing difficulty
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In: Other repository (2021)
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| 9 |
Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication ...
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| 10 |
Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication.
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| 11 |
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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| 12 |
Pronoun interpretation in Mandarin Chinese follows principles of Bayesian inference
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In: PLoS (2021)
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Abstract:
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful natural language understanding requires that comprehenders be able to resolve uncertainty in language. One source of potential uncertainty emerges from a speaker’s choice to use a pronoun (e.g., he, she, they), since pronouns often do not fully specify the speaker’s intended referent. Nevertheless, comprehenders are typically able to interpret pronouns rapidly despite having limited cognitive resources. Here we report three pronoun interpretation experiments that investigate whether comprehenders reverse-engineer a speaker’s referential intentions based on Bayesian principles, as documented in previous studies for English. Using Mandarin Chinese, we test the generality of the Bayesian pronoun interpretation theory, and further evaluate the predictions of the theory in ways that are not possible in English. Our results lend both qualitative and quantitative support to a cross-linguistically general Bayesian theory of pronoun interpretation. ; National Science Foundation (Grants BCS-1456081 and BCS-1829350) ; National Institutes of Health (Grant RO1-HD065829)
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URL: https://hdl.handle.net/1721.1/130432
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| 13 |
Assessing Language Proficiency from Eye Movements in Reading
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In: Association for Computational Linguistics (2021)
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| 14 |
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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| 15 |
Language Learning and Processing in People and Machines
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In: Association for Computational Linguistics (2021)
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| 16 |
Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing
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In: Wiley (2021)
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| 17 |
A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans ...
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| 18 |
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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