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Higher-order Derivatives of Weighted Finite-state Machines ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.acl-short.32 Abstract: Weighted finite-state machines are a fundamental building block of NLP systems. They have withstood the test of time---from their early use in noisy channel models in the 1990s up to modern-day neurally parameterized conditional random fields. This work examines the computation of higher-order derivatives with respect to the normalization constant for weighted finite-state machines. We provide a general algorithm for evaluating derivatives of all orders, which has not been previously described in the literature. In the case of second-order derivatives, our scheme runs in the optimal O(A^2 N^4) time where A is the alphabet size and N is the number of states. Our algorithm is significantly faster than prior algorithms. Additionally, our approach leads to a significantly faster algorithm for computing second-order expectations, such as covariance matrices and gradients of first-order expectations. ...
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URL: https://dx.doi.org/10.48448/b0dp-6v40
https://underline.io/lecture/25998-higher-order-derivatives-of-weighted-finite-state-machines
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Efficient computation of expectations under spanning tree distributions ...
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Higher-order Derivatives of Weighted Finite-state Machines ...
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On Finding the K-best Non-projective Dependency Trees
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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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Higher-order Derivatives of Weighted Finite-state Machines
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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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Efficient computation of expectations under spanning tree distributions
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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| 11 |
Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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| 12 |
Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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If beam search is the answer, what was the question?
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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A Joint Model of Orthography and Morphological Segmentation
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Cotterell, Ryan; Vieira, Tim; Schütze, Hinrich. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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