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1
Estimating the Entropy of Linguistic Distributions ...
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2
On Homophony and Rényi Entropy ...
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3
On Homophony and Rényi Entropy ...
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4
Revisiting the Uniform Information Density Hypothesis ...
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5
Revisiting the Uniform Information Density Hypothesis ...
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6
Conditional Poisson Stochastic Beams ...
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7
Language Model Evaluation Beyond Perplexity ...
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8
A surprisal--duration trade-off across and within the world's languages ...
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 ...
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10
Is Sparse Attention more Interpretable? ...
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11
A Cognitive Regularizer for Language Modeling ...
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12
A Cognitive Regularizer for Language Modeling ...
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13
If beam search is the answer, what was the question?
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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