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1
Linguistic communication as (inverse) reward design ...
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2
Probing BERT's priors with serial reproduction chains ...
Abstract: Sampling is a promising bottom-up method for exposing what generative models have learned about language, but it remains unclear how to generate representative samples from popular masked language models (MLMs) like BERT. The MLM objective yields a dependency network with no guarantee of consistent conditional distributions, posing a problem for naive approaches. Drawing from theories of iterated learning in cognitive science, we explore the use of serial reproduction chains to sample from BERT's priors. In particular, we observe that a unique and consistent estimator of the ground-truth joint distribution is given by a Generative Stochastic Network (GSN) sampler, which randomly selects which token to mask and reconstruct on each step. We show that the lexical and syntactic statistics of sentences from GSN chains closely match the ground-truth corpus distribution and perform better than other methods in a large corpus of naturalness judgments. Our findings establish a firmer theoretical foundation for ... : Findings of ACL 2022 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2202.12226
https://arxiv.org/abs/2202.12226
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3
From partners to populations: A hierarchical Bayesian account of coordination and convention ...
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4
Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task ...
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5
Evaluating Models of Robust Word Recognition with Serial Reproduction ...
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6
Learning Rewards from Linguistic Feedback ...
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7
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks ...
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8
Universal linguistic inductive biases via meta-learning ...
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9
Investigating representations of verb bias in neural language models ...
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10
Intuitive Theories as Grammars for Causal Inference
Tenenbaum, Joshua B.; Griffiths, Thomas L.; Niyogi, Sourabh. - : Oxford University Press, 2019
In: MIT web domain (2019)
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11
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels ...
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12
Formalizing Prior Knowledge in Casual Induction
In: The Oxford handbook of causal reasoning (2017), S. 115-126
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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13
Word forms - not just their lengths- are optimized for efficient communication ...
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14
The Hierarchical Cortical Organization of Human Speech Processing
de Heer, Wendy A.; Huth, Alexander G.; Griffiths, Thomas L.. - : Society for Neuroscience, 2017
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15
Natural speech reveals the semantic maps that tile human cerebral cortex.
In: Nature, vol 532, iss 7600 (2016)
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16
The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color
In: Cibelli, Emily; Xu, Yang; Austerweil, Joseph L; Griffiths, Thomas L; & Regier, Terry. (2016). The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color. PLOS ONE, 11(7), e0158725. doi:10.1371/journal.pone.0158725. UC Berkeley: UC Berkeley Library. Retrieved from: http://www.escholarship.org/uc/item/1pt8b5dj (2016)
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17
The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color.
In: PloS one, vol 11, iss 7 (2016)
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18
The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color
Cibelli, Emily; Xu, Yang; Austerweil, Joseph L.. - : Public Library of Science, 2016
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19
Natural speech reveals the semantic maps that tile human cerebral cortex
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20
Infant directed speech is consistent with teaching ...
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