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Findings of the 2019 Conference on Machine Translation (WMT19)

Barrault, Loïc orcid logoORCID: 0000-0002-0634-6147, Bojar, Ondřej orcid logoORCID: 0000-0002-0606-0050, Costa-Jussà, Marta R. orcid logoORCID: 0000-0002-5703-520X, Federmann, Christian, Fishel, Mark and Graham, Yvette (2019) Findings of the 2019 Conference on Machine Translation (WMT19). In: Fourth Conference on Machine Translation, 1-2 Aug 2019, Florence, Italy.

Abstract
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:UNSPECIFIED
Published in: Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:http://www.aclweb.org/anthology/W19-5301
Copyright Information:© 2019 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Union’s Horizon 2020 research and innovation programme under grant agreement Nos. 825299 (GoURMET) and 825303 (Bergamot), Connecting Europe Facility under agreement No. NEA/CEF/ICT/A2016/1331648 (ParaCrawl)., SFI Research Centres Programme (Grant 13/RC/2106) co-funded under the European Regional Development Fund., Czech Science Foundation grant no. 19-26934X (NEUREM3), Spanish Ministerio de Economía y Competitividad, the European Regional Development Fund and the Agencia Estatal de Investigación, CAS18/00223, the contract TEC2015-69266-P 36 (MINECO/FEDER,EU) and the contract PCIN2017-079 (AEI/MINECO)., German research foundation (DFG) under grant number GE 2819/2-1 (project MMPE), German Federal Ministry of Education and Research (BMBF) under funding code 01IW17001 (project Deeplee).
ID Code:24161
Deposited On:22 Jan 2020 13:56 by Vidatum Academic . Last Modified 25 Nov 2020 14:36
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