1 code implementation • LREC 2022 • Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, He Wang, Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, Hans Uszkoreit
Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese–English.
1 code implementation • LREC 2022 • Roland Roller, Aljoscha Burchardt, Nils Feldhus, Laura Seiffe, Klemens Budde, Simon Ronicke, Bilgin Osmanodja
In recent years, machine learning for clinical decision support has gained more and more attention.
Explainable Artificial Intelligence (XAI) Feature Importance
no code implementations • 1 Sep 2022 • Iyadh Ben Cheikh Larbi, Aljoscha Burchardt, Roland Roller
Clinical text processing has gained more and more attention in recent years.
no code implementations • 27 Apr 2022 • Roland Roller, Klemens Budde, Aljoscha Burchardt, Peter Dabrock, Sebastian Möller, Bilgin Osmanodja, Simon Ronicke, David Samhammer, Sven Schmeier
Scientific publications about machine learning in healthcare are often about implementing novel methods and boosting the performance - at least from a computer science perspective.
no code implementations • WMT (EMNLP) 2020 • Eleftherios Avramidis, Vivien Macketanz, Ursula Strohriegel, Aljoscha Burchardt, Sebastian Möller
This paper describes a test suite submission providing detailed statistics of linguistic performance for the state-of-the-art German-English systems of the Fifth Conference of Machine Translation (WMT20).
no code implementations • WS 2018 • Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, Hans Uszkoreit
We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite.
no code implementations • CONLL 2017 • Daniel Zeman, Martin Popel, Milan Straka, Jan Haji{\v{c}}, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinkov{\'a}, Jan Haji{\v{c}} jr., Jaroslava Hlav{\'a}{\v{c}}ov{\'a}, V{\'a}clava Kettnerov{\'a}, Zde{\v{n}}ka Ure{\v{s}}ov{\'a}, Jenna Kanerva, Stina Ojala, Anna Missil{\"a}, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Leung, Marie-Catherine de Marneffe, Manuela Sanguinetti, Maria Simi, Hiroshi Kanayama, Valeria de Paiva, Kira Droganova, H{\'e}ctor Mart{\'\i}nez Alonso, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Umut Sulubacak, Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, M, Michael l, Jesse Kirchner, Hector Fern Alcalde, ez, Jana Strnadov{\'a}, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendon{\c{c}}a, L, Tatiana o, Rattima Nitisaroj, Josie Li
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
no code implementations • LREC 2016 • Rosa Gaudio, Aljoscha Burchardt, Ant{\'o}nio Branco
In this document we report on a user-scenario-based evaluation aiming at assessing the performance of machine translation (MT) systems in a real context of use.
no code implementations • LREC 2016 • Nora Aranberri, Eleftherios Avramidis, Aljoscha Burchardt, Ond{\v{r}}ej Klejch, Martin Popel, Maja Popovi{\'c}
This work addresses the need to aid Machine Translation (MT) development cycles with a complete workflow of MT evaluation methods.
no code implementations • LREC 2014 • Eleftherios Avramidis, Aljoscha Burchardt, Sabine Hunsicker, Maja Popovi{\'c}, Cindy Tscherwinka, David Vilar, Hans Uszkoreit
Human translators are the key to evaluating machine translation (MT) quality and also to addressing the so far unanswered question when and how to use MT in professional translation workflows.
no code implementations • LREC 2012 • Eleftherios Avramidis, Aljoscha Burchardt, Christian Federmann, Maja Popovi{\'c}, Cindy Tscherwinka, David Vilar
Significant breakthroughs in machine translation only seem possible if human translators are taken into the loop.