DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5 6
Hits 1 – 20 of 101

1
Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics: Dagstuhl Seminar 21351
In: Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03507948 ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics, Aug 2021, pp.89--138, 2021, 2192-5283. ⟨10.4230/DagRep.11.7.89⟩ ; https://gitlab.com/unlid/dagstuhl-seminar/-/wikis/home (2021)
BASE
Show details
2
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
3
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
BASE
Show details
4
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
BASE
Show details
5
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
BASE
Show details
6
Universal Dependencies ...
BASE
Show details
7
Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration ...
Basirat, Ali; Nivre, Joakim. - : arXiv, 2021
BASE
Show details
8
Revisiting Negation in Neural Machine Translation ...
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
9
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
10
Attention Can Reflect Syntactic Structure (If You Let It) ...
BASE
Show details
11
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
12
Schrödinger's Tree -- On Syntax and Neural Language Models ...
Kulmizev, Artur; Nivre, Joakim. - : arXiv, 2021
BASE
Show details
13
I’ve got a construction looks funny – representing and recovering non-standard constructions in UD
Ruppenhofer, Josef [Verfasser]; Rehbein, Ines [Verfasser]; de Marneffe, Marie-Catherine [Herausgeber]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2020
DNB Subject Category Language
Show details
14
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
BASE
Show details
15
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
BASE
Show details
16
Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding ...
BASE
Show details
17
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English ...
BASE
Show details
18
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
19
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ...
BASE
Show details
20
Do Neural Language Models Show Preferences for Syntactic Formalisms? ...
BASE
Show details

Page: 1 2 3 4 5 6

Catalogues
10
1
8
0
5
0
1
Bibliographies
20
0
0
0
0
0
0
0
2
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
62
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern