"Bilingual Expert" Can Find Translation Errors

Understand

Recent advances in statistical machine translation via the adoption of neural sequence-to-sequence models empower the end-to-end system to achieve state-of-the-art in many WMT benchmarks.

• The performance of such machine translation (MT) system is usually evaluated by automatic metric BLEU when the golden references are provided for validation.
• However, for model inference or production deployment, the golden references are prohibitively available or require expensive human annotation with bilingual expertise.
• In order to address the issue of quality evaluation (QE) without reference, we propose a general framework for automatic evaluation of translation output for most WMT quality evaluation tasks.