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Hits 21 – 40 of 163

21
A Bayesian Framework for Information-Theoretic Probing ...
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22
Classifying Dyads for Militarized Conflict Analysis ...
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23
Higher-order Derivatives of Weighted Finite-state Machines ...
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24
On Finding the K-best Non-projective Dependency Trees ...
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25
A surprisal--duration trade-off across and within the world's languages ...
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26
Determinantal Beam Search ...
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27
Is Sparse Attention more Interpretable? ...
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28
Revisiting the Uniform Information Density Hypothesis ...
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29
A Plug-and-Play Method for Controlled Text Generation ...
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30
Language Model Evaluation Beyond Perplexity ...
Meister, Clara Isabel; Cotterell, Ryan. - : ETH Zurich, 2021
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31
What About the Precedent: An Information-Theoretic Analysis of Common Law ...
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32
Searching for More Efficient Dynamic Programs ...
Vieira, Tim; Cotterell, Ryan; Eisner, Jason. - : ETH Zurich, 2021
Abstract: Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search—like the mental search performed by human programmers—can find substantial improvements to the initial program. Empirically, we show that many speed-ups described in the NLP literature could have been discovered ... : Findings of the Association for Computational Linguistics: EMNLP 2021 ...
URL: http://hdl.handle.net/20.500.11850/518987
https://dx.doi.org/10.3929/ethz-b-000518987
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33
Modeling the Unigram Distribution ...
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34
Determinantal Beam Search ...
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35
Examining the Inductive Bias of Neural Language Models with Artificial Languages ...
White, Jennifer C.; Cotterell, Ryan. - : ETH Zurich, 2021
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36
Finding Concept-specific Biases in Form–Meaning Associations ...
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37
Differentiable subset pruning of transformer heads ...
Li, Jiaoda; Cotterell, Ryan; Sachan, Mrinmaya. - : ETH Zurich, 2021
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38
On Finding the K-best Non-projective Dependency Trees ...
Zmigrod, Ran; Vieira, Tim; Cotterell, Ryan. - : ETH Zurich, 2021
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39
Efficient computation of expectations under spanning tree distributions ...
Zmigrod, Ran; Vieira, Tim; Cotterell, Ryan. - : ETH Zurich, 2021
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40
Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts ...
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