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1
Dependency locality as an explanatory principle for word order
In: Prof. Levy (2022)
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2
A Systematic Assessment of Syntactic Generalization in Neural Language Models
In: Association for Computational Linguistics (2021)
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3
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
In: Association for Computational Linguistics (2021)
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4
Cognitive Science Honors the Memory of Jeffrey Elman
In: MIT Press (2021)
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5
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
In: Association for Computational Linguistics (2021)
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6
Neural language models as psycholinguistic subjects: Representations of syntactic state
In: Association for Computational Linguistics (2021)
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7
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
In: Association for Computational Linguistics (2021)
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8
Maze Made Easy: Better and easier measurement of incremental processing difficulty
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
In: Prof. Fedorenko (2021)
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12
Pronoun interpretation in Mandarin Chinese follows principles of Bayesian inference
In: PLoS (2021)
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13
Assessing Language Proficiency from Eye Movements in Reading
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
In: Sage (2021)
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15
Language Learning and Processing in People and Machines
In: Association for Computational Linguistics (2021)
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16
Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing
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
In: Proceedings of the Society for Computation in Linguistics (2021)
Abstract: What computational principles underlie human pragmatic reasoning? A prominent approach to pragmatics is the Rational Speech Act (RSA) framework, which formulates pragmatic reasoning as probabilistic speakers and listeners recursively reasoning about each other. While RSA enjoys broad empirical support, it is not yet clear whether the dynamics of such recursive reasoning may be governed by a general optimization principle. Here, we present a novel analysis of the RSA framework that addresses this question. First, we show that RSA recursion implements an alternating maximization for optimizing a tradeoff between expected utility and communicative effort. On that basis, we study the dynamics of RSA recursion and disconfirm the conjecture that expected utility is guaranteed to improve with recursion depth. Second, we show that RSA can be grounded in Rate-Distortion theory, while maintaining a similar ability to account for human behavior and avoiding a bias of RSA toward random utterance production. This work furthers the mathematical understanding of RSA models, and suggests that general information-theoretic principles may give rise to human pragmatic reasoning.
Keyword: Computational Linguistics; efficient coding; information theory; pragmatics; rational speech act
URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1195&context=scil
https://scholarworks.umass.edu/scil/vol4/iss1/32
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19
Towards a Referential Theory of Ellipsis
Poppels, Till. - : eScholarship, University of California, 2020
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20
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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