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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 Page: 1 2 3 Hits 1 – 20 of 45 1 On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ... Williams, Adina; Cotterell, Ryan; Wolf-Sonkin, Lawrence. - : arXiv, 2020 BASE Show details 2 Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ... Leung, Jun Yen; Emerson, Guy; Cotterell, Ryan. - : arXiv, 2020 BASE Show details 3 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 4 Intrinsic Probing through Dimension Selection ... Hennigen, Lucas Torroba; Williams, Adina; Cotterell, Ryan. - : arXiv, 2020 BASE Show details 5 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 6 The Paradigm Discovery Problem ... Erdmann, Alexander; Elsner, Micha; Wu, Shijie. - : ETH Zurich, 2020 BASE Show details 7 Information-Theoretic Probing for Linguistic Structure ... Pimentel, Tiago; Valvoda, Josef; Maudslay, Rowan Hall. - : arXiv, 2020 BASE Show details 8 It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information ... Bugliarello, Emanuele; Mielke, Sabrina J.; Anastasopoulos, Antonios. - : ETH Zurich, 2020 BASE Show details 9 Information-Theoretic Probing for Linguistic Structure ... Pimentel, Tiago; Valvoda, Josef; Maudslay, Rowan H.. - : ETH Zurich, 2020 BASE Show details 10 Intrinsic Probing through Dimension Selection ... Torroba Hennigen, Lucas; Williams, Adina; Cotterell, Ryan. - : ETH Zurich, 2020 BASE Show details 11 Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing ... Meister, Clara Isabel; Salesky, Elizabeth; Cotterell, Ryan. - : ETH Zurich, 2020 BASE Show details 12 A Corpus for Large-Scale Phonetic Typology ... Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - : arXiv, 2020 BASE Show details 13 Phonotactic Complexity and its Trade-offs ... Pimentel, Tiago; Roark, Brian; Cotterell, Ryan. - : arXiv, 2020 BASE Show details 14 Phonotactic Complexity and Its Trade-offs ... Pimentel, Tiago; Roark, Brian; Cotterell, Ryan. - : ETH Zurich, 2020 BASE Show details 15 A Corpus for Large-Scale Phonetic Typology ... Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - : ETH Zurich, 2020 BASE Show details 16 Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ... Leung, Jun Yen; Emerson, Guy; Cotterell, Ryan. - : ETH Zurich, 2020 BASE Show details 17 Morphologically Aware Word-Level Translation ... Czarnowska, Paula; Ruder, Sebastian; Cotterell, Ryan. - : arXiv, 2020 BASE Show details 18 Predicting Declension Class from Form and Meaning ... Williams, Adina; Pimentel, Tiago; McCarthy, Arya D.. - : arXiv, 2020 BASE Show details 19 Predicting declension class from form and meaning Williams, Adina; Pimentel, Tiago; McCarthy, Arya. - 2020 BASE Show details 20 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) Abstract: The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases.In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime. URL: https://doi.org/10.3929/ethz-b-000462321 https://hdl.handle.net/20.500.11850/462321 BASE Hide details Page: 1 2 3

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