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
Venues:
EMNLP | 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://aclanthology.org/2021.wmt-1.94.pdf