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Simple Search
Hits 1 – 13 of 13
1
Estimating the Entropy of Linguistic Distributions ...
Arora, Aryaman
;
Meister, Clara
;
Cotterell, Ryan
. - : arXiv, 2022
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2
On Homophony and Rényi Entropy ...
Pimentel, Tiago
;
Meister, Clara
;
Teufel, Simone
. - : arXiv, 2021
BASE
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3
On Homophony and Rényi Entropy ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cotterell, Ryan
;
Meister, Clara
. - : Underline Science Inc., 2021
BASE
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4
Revisiting the Uniform Information Density Hypothesis ...
Meister, Clara
;
Pimentel, Tiago
;
Haller, Patrick
. - : arXiv, 2021
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5
Revisiting the Uniform Information Density Hypothesis ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Cotterell, Ryan
;
Haller, Patrick
. - : Underline Science Inc., 2021
BASE
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6
Conditional Poisson Stochastic Beams ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Amini, Afra
;
Cotterell, Ryan
. - : Underline Science Inc., 2021
BASE
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7
Language Model Evaluation Beyond Perplexity ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Cotterell, Ryan
;
Meister, Clara
. - : Underline Science Inc., 2021
BASE
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8
A surprisal--duration trade-off across and within the world's languages ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Blasi, Damián
;
Cotterell, Ryan
;
Meister, Clara
;
Pimentel, Tiago
;
Salesky, Elizabeth
;
Teufel, Simone
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.73/ Abstract: While there exist scores of natural languages, each with its unique features and idiosyncrasies, they all share a unifying theme: enabling human communication. We may thus reasonably predict that human cognition shapes how these languages evolve and are used. Assuming that the capacity to process information is roughly constant across human populations, we expect a surprisal--duration trade-off to arise both across and within languages. We analyse this trade-off using a corpus of 600 languages and, after controlling for several potential confounds, we find strong supporting evidence in both settings. Specifically, we find that, on average, phones are produced faster in languages where they are less surprising, and vice versa. Further, we confirm that more surprising phones are longer, on average, in 319 languages out of the 600. We thus conclude that there is strong evidence of a surprisal--duration trade-off in operation, both ...
Keyword:
Cognitive Modeling
;
Computational Linguistics
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://underline.io/lecture/37827-a-surprisal--duration-trade-off-across-and-within-the-world's-languages
https://dx.doi.org/10.48448/799j-9p13
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9
Determinantal Beam Search ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Cotterell, Ryan
;
Forster, Martina
. - : Underline Science Inc., 2021
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10
Is Sparse Attention more Interpretable? ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Augenstein, Isabelle
;
Cotterell, Ryan
. - : Underline Science Inc., 2021
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11
A Cognitive Regularizer for Language Modeling ...
Wei, Jason
;
Meister, Clara
;
Cotterell, Ryan
. - : arXiv, 2021
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12
A Cognitive Regularizer for Language Modeling ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Cotterell, Ryan
;
Meister, Clara
. - : Underline Science Inc., 2021
BASE
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13
If beam search is the answer, what was the question?
Meister, Clara
;
Cotterell, Ryan
;
Vieira, Tim
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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