CUNI Systems for the Unsupervised News Translation Task in WMT 2019

Ivana Kvapilíková, Dominik Macháček, Ondřej Bojar


Abstract
In this paper we describe the CUNI translation system used for the unsupervised news shared task of the ACL 2019 Fourth Conference on Machine Translation (WMT19). We follow the strategy of Artetxe ae at. (2018b), creating a seed phrase-based system where the phrase table is initialized from cross-lingual embedding mappings trained on monolingual data, followed by a neural machine translation system trained on synthetic parallel data. The synthetic corpus was produced from a monolingual corpus by a tuned PBMT model refined through iterative back-translation. We further focus on the handling of named entities, i.e. the part of vocabulary where the cross-lingual embedding mapping suffers most. Our system reaches a BLEU score of 15.3 on the German-Czech WMT19 shared task.
Anthology ID:
W19-5323
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
241–248
Language:
URL:
https://aclanthology.org/W19-5323
DOI:
10.18653/v1/W19-5323
Bibkey:
Cite (ACL):
Ivana Kvapilíková, Dominik Macháček, and Ondřej Bojar. 2019. CUNI Systems for the Unsupervised News Translation Task in WMT 2019. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 241–248, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
CUNI Systems for the Unsupervised News Translation Task in WMT 2019 (Kvapilíková et al., WMT 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ml4al-ingestion/W19-5323.pdf
Poster:
 W19-5323.Poster.pdf