  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.
