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Hits 1 – 20 of 22
1
Estimating the Entropy of Linguistic Distributions ...
Arora, Aryaman
;
Meister, Clara
;
Cotterell, Ryan
. - : arXiv, 2022
BASE
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2
On Homophony and Rényi Entropy ...
Pimentel, Tiago
;
Meister, Clara Isabel
;
Teufel, Simone
. - : ETH Zurich, 2021
BASE
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3
On Homophony and Rényi Entropy ...
Pimentel, Tiago
;
Meister, Clara
;
Teufel, Simone
. - : arXiv, 2021
BASE
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4
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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5
Searching for Search Errors in Neural Morphological Inflection ...
Forster, Martina
;
Meister, Clara Isabel
;
Cotterell, Ryan
. - : ETH Zurich, 2021
BASE
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6
Revisiting the Uniform Information Density Hypothesis ...
Meister, Clara
;
Pimentel, Tiago
;
Haller, Patrick
. - : arXiv, 2021
BASE
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7
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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8
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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9
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
Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.414 Abstract: We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated text on which they were trained. We provide a framework--paired with significance tests--for evaluating the fit of language models to these trends. We find that neural language models appear to learn only a subset of the tendencies considered, but align much more closely with empirical trends than proposed theoretical distributions (when present). Further, the fit to different distributions is highly-dependent on both model architecture and generation strategy. As concrete examples, text generated under the nucleus sampling scheme adheres more closely to the type--token relationship of natural language than text produced using ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://underline.io/lecture/25838-language-model-evaluation-beyond-perplexity
https://dx.doi.org/10.48448/jr48-6p89
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10
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
. - : Underline Science Inc., 2021
BASE
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11
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
BASE
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12
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
BASE
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13
Revisiting the Uniform Information Density Hypothesis ...
Meister, Clara Isabel
;
Pimentel, Tiago
;
Haller, Patrick
. - : ETH Zurich, 2021
BASE
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14
A Plug-and-Play Method for Controlled Text Generation ...
Pascual, Damian
;
Egressy, Béni
;
Meister, Clara Isabel
. - : ETH Zurich, 2021
BASE
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15
Language Model Evaluation Beyond Perplexity ...
Meister, Clara Isabel
;
Cotterell, Ryan
. - : ETH Zurich, 2021
BASE
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16
Determinantal Beam Search ...
Meister, Clara Isabel
;
Forster, Martina
;
Cotterell, Ryan
. - : ETH Zurich, 2021
BASE
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17
Is Sparse Attention more Interpretable? ...
Meister, Clara Isabel
;
Lazov, Stefan
;
Augenstein, Isabelle
. - : ETH Zurich, 2021
BASE
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18
A Cognitive Regularizer for Language Modeling ...
Wei, Jason
;
Meister, Clara
;
Cotterell, Ryan
. - : arXiv, 2021
BASE
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19
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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20
A Cognitive Regularizer for Language Modeling ...
Wei, Jason
;
Meister, Clara Isabel
;
Cotterell, Ryan
. - : ETH Zurich, 2021
BASE
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