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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 Hits 1 – 20 of 24 1 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 2 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 The Impact of Positional Encodings on Multilingual Compression ... Ravishankar, Vinit; Søgaard, Anders. - : arXiv, 2021 BASE Show details 5 The Impact of Positional Encodings on Multilingual Compression ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ravishankar, Vinit; Søgaard, Anders. - : Underline Science Inc., 2021 BASE Show details 6 Attention Can Reflect Syntactic Structure (If You Let It) ... Ravishankar, Vinit; Kulmizev, Artur; Abdou, Mostafa. - : arXiv, 2021 BASE Show details 7 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 8 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 9 The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ... Abdou, Mostafa; Ravishankar, Vinit; Barrett, Maria; Belinkov, Yonatan; Elliott, Desmond; Søgaard, Anders. - : arXiv, 2020 Abstract: Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. ... : ACL 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2005.01348 https://arxiv.org/abs/2005.01348 BASE Hide details 10 From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ... Lauscher, Anne; Ravishankar, Vinit; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 11 From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ... Lauscher, Anne; Ravishankar, Vinit; Vulić, Ivan. - : arXiv, 2020 BASE Show details 12 Do Neural Language Models Show Preferences for Syntactic Formalisms? ... Kulmizev, Artur; Ravishankar, Vinit; Abdou, Mostafa. - : arXiv, 2020 BASE Show details 13 From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers Ravishankar, Vinit; Glavas, Goran; Lauscher, Anne. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 14 From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers Ravishankar, Vinit; Glavaš, Goran; Lauscher, Anne. - : Association for Computational Linguistics, 2020 BASE Show details 15 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 16 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 17 Probing Multilingual Sentence Representations With X-Probe ... Ravishankar, Vinit; Øvrelid, Lilja; Velldal, Erik. - : arXiv, 2019 BASE Show details 18 The WMT'18 Morpheval test suites for English-Czech, English-German, English-Finnish and Turkish-English Burlot, Franck; Scherrer, Yves; Ravishankar, Vinit... In: Proceedings of the Third Conference on Machine Translation ; 3rd Conference on Machine Translation (WMT 18) ; https://hal.archives-ouvertes.fr/hal-01910244 ; 3rd Conference on Machine Translation (WMT 18), Oct 2018, Bruxelles, Belgium. pp.550-564, ⟨10.18653/v1/W18-64060⟩ ; http://www.statmt.org/wmt18/ (2018) BASE Show details 19 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko... In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018) BASE Show details 20 Universal Dependencies 2.3 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details Page: 1 2

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