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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 4 5 Hits 1 – 20 of 94 1 Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics (Dagstuhl Seminar 21351) Croft, William; Savary, Agata; Baldwin, Timothy. - : Dagstuhl Reports. DagRep, Volume 11, Issue 7, 2021 BASE Show details 2 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 Universal Dependencies ... NAACL 2021 2021; de Marneffe, Marie-Catherine; Manning, Christopher. - : Underline Science Inc., 2021 BASE Show details 6 Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration ... Basirat, Ali; Nivre, Joakim. - : arXiv, 2021 BASE Show details 7 Revisiting Negation in Neural Machine Translation ... 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; Nivre, Joakim; Rönchen, Philipp; Sennrich, Rico; Tang, Gongbo. - : Underline Science Inc., 2021 Abstract: Read paper: NA Abstract: In this paper, we evaluate the translation of negation both automatically and manually, in English—German (EN—DE) and English—Chinese (EN—ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN—DE, DE—EN, EN—ZH, and ZH—EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model's information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25803-revisiting-negation-in-neural-machine-translation https://dx.doi.org/10.48448/x27r-fa09 BASE Hide details 8 Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics (Dagstuhl Seminar 21351) ... Baldwin, Timothy; Croft, William; Nivre, Joakim. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021 BASE Show details 9 Attention Can Reflect Syntactic Structure (If You Let It) ... Ravishankar, Vinit; Kulmizev, Artur; Abdou, Mostafa. - : arXiv, 2021 BASE Show details 10 What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions? ... NAACL 2021 2021; de Lhoneux, Miryam; Nivre, Joakim. - : Underline Science Inc., 2021 BASE Show details 11 Schrödinger's Tree -- On Syntax and Neural Language Models ... Kulmizev, Artur; Nivre, Joakim. - : arXiv, 2021 BASE Show details 12 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 13 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 14 Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding ... Hershcovich, Daniel; de Lhoneux, Miryam; Kulmizev, Artur. - : arXiv, 2020 BASE Show details 15 Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English ... Tang, Gongbo; Sennrich, Rico; Nivre, Joakim. - : arXiv, 2020 BASE Show details 16 Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English ... Tang, Gongbo; Sennrich, Rico; Nivre, Joakim. - : International Committee on Computational Linguistics, 2020 BASE Show details 17 Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ... Nivre, Joakim; de Marneffe, Marie-Catherine; Ginter, Filip. - : arXiv, 2020 BASE Show details 18 Do Neural Language Models Show Preferences for Syntactic Formalisms? ... Kulmizev, Artur; Ravishankar, Vinit; Abdou, Mostafa. - : arXiv, 2020 BASE Show details 19 Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English Tang, Gongbo; Sennrich, Rico; Nivre, Joakim In: Tang, Gongbo; Sennrich, Rico; Nivre, Joakim (2020). Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English. In: Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, 8 December 2020 - 13 December 2020, 4251-4262. (2020) BASE Show details 20 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details Page: 1 2 3 4 5

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