Quality Estimation Shared Task

Quality Estimation (QE) focuses on automatically assessing the quality of machine translations or other generated content without relying on reference translations, aiming to improve the efficiency and accuracy of quality control processes. Current research emphasizes leveraging deep learning models, particularly pre-trained language models and convolutional neural networks, often incorporating self-supervised learning and techniques like disentangled representation learning to improve prediction accuracy and efficiency. These advancements have led to state-of-the-art performance in various QE tasks, including sentence-level and word-level quality prediction, as well as fine-grained error detection, impacting both machine translation and broader applications requiring automated quality assessment.