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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) BASE Show 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.; Zmigrod, Ran; Williams, Adina; Cotterell, Ryan. - : ETH Zurich, 2020 Abstract: The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually "know" about natural language. Probes are a natural way of assessing this. When probing, a researcher chooses a linguistic task and trains a supervised model to predict annotations in that linguistic task from the network's learned representations. If the probe does well, the researcher may conclude that the representations encode knowledge related to the task. A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task itself. We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation. The ... : Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ... URL: http://hdl.handle.net/20.500.11850/446005 https://dx.doi.org/10.3929/ethz-b-000446005 BASE Hide 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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