Case Study: Ariel Corporation improves translation quality and reduces post-editing with SYSTRAN and XTM

Ariel Corporation boosts translation quality by 22 BLEU points with SYSTRAN

Ariel Corporation wanted to enhance the value of its existing language assets and improve machine translation quality within its automated content workflow. The company partnered with SYSTRAN to integrate customized neural machine translation with its XTM translation management system.

SYSTRAN's solution combined its neural machine translation engine with XTM's AI-enhanced translation memory feature. This sent fuzzy matches from validated translation memories to the MT engine as reference material, optimizing translation quality in real time and adapting to the author's style. The result was an expected increase of +22 BLEU points in translation quality, leading to a significant reduction in post-editing effort and cost savings for Ariel Corporation.

Ariel Corporation