The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task

Shuoyang Ding, Marcin Junczys-Dowmunt, Matt Post, Christian Federmann, Philipp Koehn


Abstract
This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The techniques we experimented with include Levenshtein Transformer training and data augmentation with a combination of forward, backward, round-trip translation, and pseudo post-editing of the MT output. We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline. Our system is also the top-ranking system on the MT MCC metric for the English-German language pair.
Anthology ID:
2021.wmt-1.94
Volume:
Proceedings of the Sixth Conference on Machine Translation
Month:
November
Year:
2021
Address:
Online
Editors:
Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
904–910
Language:
URL:
https://aclanthology.org/2021.wmt-1.94
DOI:
Bibkey:
Cite (ACL):
Shuoyang Ding, Marcin Junczys-Dowmunt, Matt Post, Christian Federmann, and Philipp Koehn. 2021. The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 904–910, Online. Association for Computational Linguistics.
Cite (Informal):
The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task (Ding et al., WMT 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2021.wmt-1.94.pdf