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arXiv · 2005.03519

Practical Perspectives on Quality Estimation for Machine Translation

Junpei Zhou·Ciprian Chelba·Yuezhang·Li
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Abstract

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.

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Junpei Zhou, Ciprian Chelba, Yuezhang, Li. 2020-05-02. Practical Perspectives on Quality Estimation for Machine Translation. https://arxiv.org/abs/2005.03519

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