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.

本文提出了一种称为扩展单词对齐的新概念，以提高后编辑辅助效率，并应用于一项新任务 - 精化的单词级质量评估。文章采用基于 mBERT 的监督方法提取扩展单词对齐，用回归模型训练 mBERT 和 XLM-R 进行序列标注来解决单词级质量评估问题，并通过扩展单词对齐实现对原单词标签的精化。实验结果表明，该方法可行并为进一步改进提供灵感。