Data Augmentation with Translation Memories for Desktop Machine Translation Fine-tuning in 3 Language Pairs

Abstract

This study aims to investigate the effect of data augmentation through translation memories for desktop machine translation (MT) fine-tuning in OPUS-CAT. It also focuses on assessing the usefulness of desktop MT for professional translators. Engines in three language pairs (English → Turkish, English → Spanish, and English → Catalan) are fine-tuned with corpora of two different sizes. The translation quality of each engine is measured through automatic evaluation metrics (BLEU, chrF2, TER and COMET) and human evaluation metrics (ranking, adequacy and fluency). Overall evaluation results indicate promising quality improvements in all three language pairs and imply that the use of desktop MT applications such as OPUS-CAT and fine-tuning MT engines with custom data in a translator’s desktop can potentially provide high-quality translations aside from their advantages such as privacy, confidentiality and low use of computation power.

Keywords

• machine translation fine-tuning
• domain adaptation
• desktop machine translation
• localization
• parallel corpora
• professional translators