Quality Estimation without Human-labeled Data

Understand

Quality estimation aims to measure the quality of translated content without access to a reference translation.

• This is crucial for machine translation systems in real-world scenarios where high-quality translation is needed.
• While many approaches exist for quality estimation, they are based on supervised machine learning requiring costly human labelled data.
• As an alternative, we propose a technique that does not rely on examples from human-annotators and instead uses synthetic training data.