Sentence level quality estimation (QE) for machine translation (MT) attempts
to predict the translation edit rate (TER) cost of post-editing work required
to correct MT output. We describe our view on sentence-level QE as dictated by
several practical setups encountered in the industry. We find consumers of MT
output---whether human or algorithmic ones---to be primarily interested in a
binary quality metric: is the translated sentence adequate as-is or does it
need post-editing? Motivated by this we propose a quality classification (QC)
view on sentence-level QE whereby we focus on maximizing recall at precision
above a given threshold. We demonstrate that, while classical QE regression
models fare poorly on this task, they can be re-purposed by replacing the
output regression layer with a binary classification one, achieving 50-60\%
recall at 90\% precision. For a high-quality MT system producing 75-80\%
correct translations, this promises a significant reduction in post-editing
work indeed.

本研究旨在提高机器翻译句子水平的翻译编辑速率预测，提出了句子水平的质量分类（QC）观点，以优化召回率，并通过二进制分类器的使用可将后编辑工作量减少至 50-60％。