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Higher-order Derivatives of Weighted Finite-state Machines ...
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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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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 ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://dx.doi.org/10.48448/6ans-yx85 https://underline.io/lecture/25733-determinantal-beam-search
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31 |
What About the Precedent: An Information-Theoretic Analysis of Common Law ...
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35 |
Examining the Inductive Bias of Neural Language Models with Artificial Languages ...
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36 |
Finding Concept-specific Biases in Form–Meaning Associations ...
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39 |
Efficient computation of expectations under spanning tree distributions ...
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40 |
Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts ...
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