This paper proposes an efficient and semi-automated method for
human-in-the-loop post-editing for machine translation (MT) corpus generation.
The method is based on online training of a custom MT quality estimation metric
on-the-fly as linguists perform post-edits. The online estimator is used to
prioritize worse hypotheses for post-editing, and auto-close best hypotheses
without post-editing. This way, significant improvements can be achieved in the
resulting quality of post-edits at a lower cost due to reduced human
involvement. The trained estimator can also provide an online sanity check
mechanism for post-edits and remove the need for additional linguists to review
them or work on the same hypotheses. In this paper, the effect of prioritizing
with the proposed method on the resulting MT corpus quality is presented versus
scheduling hypotheses randomly. As demonstrated by experiments, the proposed
method improves the lifecycle of MT models by focusing the linguist effort on
production samples and hypotheses, which matter most for expanding MT corpora
to be used for re-training them.

本篇论文提出了一种有效且半自动化的方法，用于人工编辑后的机器翻译语料库生成，该方法基于在线训练定制的 MT 质量估计指标并需要人类参与，实现了有待优化问题的优先级排序和最佳解的自动处理。尤其地，该方法解决了人工编辑效率低下，从而降低了人工成本的问题，并通过实验证明了其对机器翻译模型生命周期的作用。