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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 14 of 14 1 Higher-order Derivatives of Weighted Finite-state Machines ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cotterell, Ryan; Vieira, Tim. - : Underline Science Inc., 2021 BASE Show details 2 On Finding the K-best Non-projective Dependency Trees ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cotterell, Ryan; Vieira, Tim. - : Underline Science Inc., 2021 BASE Show details 3 On Finding the K-best Non-projective Dependency Trees ... Zmigrod, Ran; Vieira, Tim; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 4 Efficient computation of expectations under spanning tree distributions ... Zmigrod, Ran; Vieira, Tim; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 5 Higher-order Derivatives of Weighted Finite-state Machines ... Zmigrod, Ran; Vieira, Tim; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 6 On Finding the K-best Non-projective Dependency Trees Zmigrod, Ran; Cotterell, Ryan; Vieira, Tim In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021) Abstract: The connection between the maximum spanning tree in a directed graph and the best dependency tree of a sentence has been exploited by the NLP community. However, for many dependency parsing schemes, an important detail of this approach is that the spanning tree must have exactly one edge emanating from the root. While work has been done to efficiently solve this problem for finding the one-best dependency tree, no research has attempted to extend this solution to finding the K-best dependency trees. This is arguably a more important extension as a larger proportion of decoded trees will not be subject to the root constraint of dependency trees. Indeed, we show that the rate of root constraint violations increases by an average of 13 times when decoding with K=50 as opposed to K=1. In this paper, we provide a simplification of the K-best spanning tree algorithm of Camerini et al. (1980). Our simplification allows us to obtain a constant time speed-up over the original algorithm. Furthermore, we present a novel extension of the algorithm for decoding the K-best dependency trees of a graph which are subject to a root constraint. URL: https://hdl.handle.net/20.500.11850/521262 https://doi.org/10.3929/ethz-b-000519003 BASE Hide details 7 Higher-order Derivatives of Weighted Finite-state Machines Zmigrod, Ran; Cotterell, Ryan; Vieira, Tim In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021) BASE Show details 8 Efficient computation of expectations under spanning tree distributions Cotterell, Ryan; Zmigrod, Ran; Vieira, Tim In: Transactions of the Association for Computational Linguistics, 9 (2021) BASE Show details 9 Efficient Sampling of Dependency Structure Vieira, Tim; Zmigrod, Ran; Cotterell, Ryan In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) BASE Show details 10 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 11 Information-Theoretic Probing for Linguistic Structure ... Pimentel, Tiago; Valvoda, Josef; Maudslay, Rowan Hall. - : arXiv, 2020 BASE Show details 12 Information-Theoretic Probing for Linguistic Structure ... Pimentel, Tiago; Valvoda, Josef; Maudslay, Rowan H.. - : ETH Zurich, 2020 BASE Show details 13 Please Mind the Root: Decoding Arborescences for Dependency Parsing Vieira, Tim; Cotterell, Ryan; Zmigrod, Ran In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 14 Information-Theoretic Probing for Linguistic Structure Pimentel, Tiago; Zmigrod, Ran; Cotterell, Ryan... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details

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