The University of Edinburgh’s Submissions to the WMT19 News Translation Task

Rachel Bawden, Nikolay Bogoychev, Ulrich Germann, Roman Grundkiewicz, Faheem Kirefu, Antonio Valerio Miceli Barone, Alexandra Birch


Abstract
The University of Edinburgh participated in the WMT19 Shared Task on News Translation in six language directions: English↔Gujarati, English↔Chinese, German→English, and English→Czech. For all translation directions, we created or used back-translations of monolingual data in the target language as additional synthetic training data. For English↔Gujarati, we also explored semi-supervised MT with cross-lingual language model pre-training, and translation pivoting through Hindi. For translation to and from Chinese, we investigated character-based tokenisation vs. sub-word segmentation of Chinese text. For German→English, we studied the impact of vast amounts of back-translated training data on translation quality, gaining a few additional insights over Edunov et al. (2018). For English→Czech, we compared different preprocessing and tokenisation regimes.
Anthology ID:
W19-5304
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
103–115
Language:
URL:
https://aclanthology.org/W19-5304
DOI:
10.18653/v1/W19-5304
Bibkey:
Cite (ACL):
Rachel Bawden, Nikolay Bogoychev, Ulrich Germann, Roman Grundkiewicz, Faheem Kirefu, Antonio Valerio Miceli Barone, and Alexandra Birch. 2019. The University of Edinburgh’s Submissions to the WMT19 News Translation Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 103–115, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
The University of Edinburgh’s Submissions to the WMT19 News Translation Task (Bawden et al., WMT 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/W19-5304.pdf