1 research outputs found
Practical Perspectives on Quality Estimation for Machine Translation
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