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Vivien Macketanz
Vivien Macketanz
Verified email at dfki.de
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Cited by
Year
CoNLL 2018 shared task: Multilingual parsing from raw text to universal dependencies
D Zeman, J Hajic, M Popel, M Potthast, M Straka, F Ginter, J Nivre, ...
Proceedings of the CoNLL 2018 Shared Task: Multilingual parsing from raw …, 2018
6812018
Universal Dependencies 1.2
J Nivre, ® Agiæ, MJ Aranzabe, M Asahara, A Atutxa, M Ballesteros, J Bauer, ...
Universal Dependencies Consortium, 2015
2272015
A linguistic evaluation of rule-based, phrase-based, and neural MT engines
A Burchardt, V Macketanz, J Dehdari, G Heigold, P Jan-Thorsten, ...
The Prague bulletin of mathematical linguistics 108 (1), 159, 2017
1112017
Machine translation: Phrase-based, rule-based and neural approaches with linguistic evaluation
V Macketanz, E Avramidis, A Burchardt, J Helcl, A Srivastava
Cybernetics and Information Technologies 17 (2), 28-43, 2017
432017
Fine-grained evaluation of German-English machine translation based on a test suite
V Macketanz, E Avramidis, A Burchardt, H Uszkoreit
arXiv preprint arXiv:1910.07460, 2019
362019
Linguistic evaluation of German-English machine translation using a test suite
E Avramidis, V Macketanz, U Strohriegel, H Uszkoreit
arXiv preprint arXiv:1910.07457, 2019
342019
Fine-grained evaluation of quality estimation for machine translation based on a linguistically motivated test suite
E Avramidis, V Macketanz, A Lommel, H Uszkoreit
Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and …, 2018
212018
Fine-grained linguistic evaluation for state-of-the-art machine translation
E Avramidis, V Macketanz, U Strohriegel, A Burchardt, S Möller
Proceedings of the Fifth Conference on Machine Translation, 346-356, 2020
202020
Can out-of-the-box NMT Beat a Domain-trained Moses on Technical Data
A Beyer, V Macketanz, A Burchardt, P Williams
20th Annual Conference of the European Association for Machine Translation, 2017
192017
Linguistically Motivated Evaluation of the 2023 State-of-the-art Machine Translation: Can ChatGPT Outperform NMT?
S Manakhimova, E Avramidis, V Macketanz, E Lapshinova-Koltunski, ...
Proceedings of the Eighth Conference on Machine Translation, 224-245, 2023
162023
Linguistic evaluation for the 2021 state-of-the-art machine translation systems for german to english and english to german
V Macketanz, E Avramidis, S Manakhimova, S Möller
Proceedings of the Sixth Conference on Machine Translation, 1059-1073, 2021
152021
Linguistically motivated evaluation of machine translation metrics based on a challenge set
E Avramidis, V Macketanz
Proceedings of the Seventh Conference on Machine Translation (WMT), 514-529, 2022
142022
Deeper machine translation and evaluation for German
E Avramidis, V Macketanz, A Burchardt, J Helcl, H Uszkoreit
Proceedings of the 2nd Deep Machine Translation Workshop, 29-38, 2016
112016
DFKI’s system for WMT16 IT-domain task, including analysis of systematic errors
E Avramidis, A Burchardt, V Macketanz, A Srivastava
Proceedings of the First Conference on Machine Translation: Volume 2, Shared …, 2016
112016
Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, and Hans Uszkoreit. 2022. A linguistically motivated test suite to semi-automatically evaluate German …
V Macketanz, E Avramidis, A Burchardt, H Wang
Proceedings of the Language Resources and Evaluation Conference, 936-947, 0
10
Train, sort, explain: Learning to diagnose translation models
R Schwarzenberg, D Harbecke, V Macketanz, E Avramidis, S Möller
arXiv preprint arXiv:1903.12017, 2019
92019
Renlong Ai, Aljoscha Burchardt, and Hans Uszkoreit. 2018a. TQ-AutoTest–an automated test suite for (machine) translation quality
V Macketanz
Proceedings of the Eleventh International Conference on Language Resources …, 0
9
A new deal for translation quality
A Burchardt, A Lommel, V Macketanz
Universal access in the information society 20, 701-715, 2021
82021
TQ-AutoTest–An Automated Test Suite for (Machine) Translation Quality
V Macketanz, R Ai, A Burchardt, H Uszkoreit
Proceedings of the Eleventh International Conference on Language Resources …, 2018
82018
TQ-AUTOTEST: Novel analytical quality measure confirms that DeepL is better than Google Translate
V Macketanz, A Burchardt, H Uszkoreit
Tech. rep. The Globalization and Localization Association (GALA). url: https …, 2018
82018
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