English-Czech Systems in WMT19: Document-Level Transformer

Martin Popel, Dominik Macháček, Michal Auersperger, Ondřej Bojar, Pavel Pecina


Abstract
We describe our NMT systems submitted to the WMT19 shared task in English→Czech news translation. Our systems are based on the Transformer model implemented in either Tensor2Tensor (T2T) or Marian framework. We aimed at improving the adequacy and coherence of translated documents by enlarging the context of the source and target. Instead of translating each sentence independently, we split the document into possibly overlapping multi-sentence segments. In case of the T2T implementation, this “document-level”-trained system achieves a +0.6 BLEU improvement (p < 0.05) relative to the same system applied on isolated sentences. To assess the potential effect document-level models might have on lexical coherence, we performed a semi-automatic analysis, which revealed only a few sentences improved in this aspect. Thus, we cannot draw any conclusions from this week evidence.
Anthology ID:
W19-5337
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:
342–348
Language:
URL:
https://aclanthology.org/W19-5337
DOI:
10.18653/v1/W19-5337
Bibkey:
Cite (ACL):
Martin Popel, Dominik Macháček, Michal Auersperger, Ondřej Bojar, and Pavel Pecina. 2019. English-Czech Systems in WMT19: Document-Level Transformer. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 342–348, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
English-Czech Systems in WMT19: Document-Level Transformer (Popel et al., WMT 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5337.pdf