  We propose a novel scheme to use the Levenshtein Transformer to perform the
task of word-level quality estimation. A Levenshtein Transformer is a natural
fit for this task: trained to perform decoding in an iterative manner, a
Levenshtein Transformer can learn to post-edit without explicit supervision. To
further minimize the mismatch between the translation task and the word-level
QE task, we propose a two-stage transfer learning procedure on both augmented
data and human post-editing data. We also propose heuristics to construct
reference labels that are compatible with subword-level finetuning and
inference. Results on WMT 2020 QE shared task dataset show that our proposed
method has superior data efficiency under the data-constrained setting and
competitive performance under the unconstrained setting.
