# Practical Perspectives on Quality Estimation for Machine Translation

arXiv: https://arxiv.org/abs/2005.03519 · HTML: https://mlsanity.com/paper/2005.03519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 1 of 1)

- [Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications](https://mlsanity.com/paper/2109.08627/) (2021)

## Abstract (arXiv)

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.