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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.512 Abstract: Beam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates. Empirically, this leads to sets often exhibiting high overlap, e.g., strings may differ by only a single word. Yet in use-cases that call for multiple solutions, a diverse or representative set is often desired. To address this issue, we propose a reformulation of beam search, which we call determinantal beam search. Determinantal beam search has a natural relationship to determinantal point processes (DPPs), models over sets that inherently encode intra-set interactions. By posing iterations in beam search as a series of subdeterminant maximization problems, we can turn the algorithm into a diverse subset selection process. In a case study, we use the string subsequence kernel to explicitly encourage ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/6ans-yx85
https://underline.io/lecture/25733-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
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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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