  We define a novel concept called extended word alignment in order to improve
post-editing assistance efficiency. Based on extended word alignment, we
further propose a novel task called refined word-level QE that outputs refined
tags and word-level correspondences. Compared to original word-level QE, the
new task is able to directly point out editing operations, thus improves
efficiency. To extract extended word alignment, we adopt a supervised method
based on mBERT. To solve refined word-level QE, we firstly predict original QE
tags by training a regression model for sequence tagging based on mBERT and
XLM-R. Then, we refine original word tags with extended word alignment. In
addition, we extract source-gap correspondences, meanwhile, obtaining gap tags.
Experiments on two language pairs show the feasibility of our method and give
us inspirations for further improvement.
