Word Level Quality

Word-level quality estimation (QE) focuses on automatically assessing the quality of individual words within machine-translated text, aiming to pinpoint errors without relying on reference translations. Recent research emphasizes improving the accuracy of word-level QE by leveraging multilingual models, incorporating both sentence- and word-level training objectives, and exploring techniques like data augmentation with carefully selected parallel corpora and synthetic data generation. These advancements are crucial for enhancing machine translation systems, enabling more efficient post-editing, and providing valuable insights into the strengths and weaknesses of translation models.