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
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 904–910
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.94
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.wmt-1.94.pdf