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Higher-order Derivatives of Weighted Finite-state Machines ...
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Efficient computation of expectations under spanning tree distributions ...
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Abstract:
We give a general framework for inference in spanning tree models. We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. Our algorithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms. These algorithms are easy to implement with or without automatic differentiation software. We motivate the development of our framework with several cautionary tales of previous research, which has developed numerous inefficient algorithms for computing expectations and their gradients. We demonstrate how our framework efficiently computes several quantities with known algorithms, including the expected attachment score, entropy, and generalized expectation criteria. As a bonus, we give algorithms for quantities that are missing in the literature, including the KL divergence. In all cases, our approach matches the efficiency of existing algorithms ... : Transactions of the Association for Computational Linguistics, 9 ...
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URL: https://dx.doi.org/10.3929/ethz-b-000517632 http://hdl.handle.net/20.500.11850/517632
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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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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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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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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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Information-Theoretic Probing for Linguistic Structure
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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