  Machine Translation Quality Estimation (MTQE) is the task of estimating the
quality of machine-translated text in real time without the need for reference
translations, which is of great importance for the development of MT. After two
decades of evolution, QE has yielded a wealth of results. This article provides
a comprehensive overview of QE datasets, annotation methods, shared tasks,
methodologies, challenges, and future research directions. It begins with an
introduction to the background and significance of QE, followed by an
explanation of the concepts and evaluation metrics for word-level QE,
sentence-level QE, document-level QE, and explainable QE. The paper categorizes
the methods developed throughout the history of QE into those based on
handcrafted features, deep learning, and Large Language Models (LLMs), with a
further division of deep learning-based methods into classic deep learning and
those incorporating pre-trained language models (LMs). Additionally, the
article details the advantages and limitations of each method and offers a
straightforward comparison of different approaches. Finally, the paper
discusses the current challenges in QE research and provides an outlook on
future research directions.
