The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task
- URL: http://arxiv.org/abs/2109.08724v1
- Date: Fri, 17 Sep 2021 19:13:31 GMT
- Title: The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task
- Authors: Shuoyang Ding, Marcin Junczys-Dowmunt, Matt Post, Christian Federmann,
Philipp Koehn
- Abstract summary: 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.
We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline.
- Score: 14.629380601429956
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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