Quality Estimator

Quality estimators are automated systems designed to assess the quality of outputs from various machine learning models, such as machine translation or grammatical error correction systems, often in the absence of readily available ground truth. Current research focuses on improving the accuracy and reliability of these estimators, exploring techniques like multi-objective reinforcement learning and novel meta-evaluation frameworks to address challenges like conflicting rankings and the lack of ground truth labels. These advancements are crucial for enhancing the trustworthiness and practical applicability of machine learning systems across diverse domains, from healthcare to cloud gaming, by enabling more informed decision-making and improved system combination strategies.