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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
On Homophony and Rényi Entropy ...
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5
Searching for Search Errors in Neural Morphological Inflection ...
Abstract: Neural sequence-to-sequence models are currently the predominant choice for language generation tasks. Yet, on word-level tasks, exact inference of these models reveals the empty string is often the global optimum. Prior works have speculated this phenomenon is a result of the inadequacy of neural models for language generation. However, in the case of morphological inflection, we find that the empty string is almost never the most probable solution under the model. Further, greedy search often finds the global optimum. These observations suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. ... : Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume ...
URL: http://hdl.handle.net/20.500.11850/518999
https://dx.doi.org/10.3929/ethz-b-000518999
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6
Revisiting the Uniform Information Density Hypothesis ...
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7
Revisiting the Uniform Information Density Hypothesis ...
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8
Conditional Poisson Stochastic Beams ...
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9
Language Model Evaluation Beyond Perplexity ...
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10
A surprisal--duration trade-off across and within the world's languages ...
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11
Determinantal Beam Search ...
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12
Is Sparse Attention more Interpretable? ...
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13
Revisiting the Uniform Information Density Hypothesis ...
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14
A Plug-and-Play Method for Controlled Text Generation ...
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15
Language Model Evaluation Beyond Perplexity ...
Meister, Clara Isabel; Cotterell, Ryan. - : ETH Zurich, 2021
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16
Determinantal Beam Search ...
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17
Is Sparse Attention more Interpretable? ...
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18
A Cognitive Regularizer for Language Modeling ...
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19
A Cognitive Regularizer for Language Modeling ...
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20
A Cognitive Regularizer for Language Modeling ...
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