  Since the 1950s, machine translation (MT) has become one of the important
tasks of AI and development, and has experienced several different periods and
stages of development, including rule-based methods, statistical methods, and
recently proposed neural network-based learning methods. Accompanying these
staged leaps is the evaluation research and development of MT, especially the
important role of evaluation methods in statistical translation and neural
translation research. The evaluation task of MT is not only to evaluate the
quality of machine translation, but also to give timely feedback to machine
translation researchers on the problems existing in machine translation itself,
how to improve and how to optimise. In some practical application fields, such
as in the absence of reference translations, the quality estimation of machine
translation plays an important role as an indicator to reveal the credibility
of automatically translated target languages. This report mainly includes the
following contents: a brief history of machine translation evaluation (MTE),
the classification of research methods on MTE, and the the cutting-edge
progress, including human evaluation, automatic evaluation, and evaluation of
evaluation methods (meta-evaluation). Manual evaluation and automatic
evaluation include reference-translation based and reference-translation
independent participation; automatic evaluation methods include traditional
n-gram string matching, models applying syntax and semantics, and deep learning
models; evaluation of evaluation methods includes estimating the credibility of
human evaluations, the reliability of the automatic evaluation, the reliability
of the test set, etc. Advances in cutting-edge evaluation methods include
task-based evaluation, using pre-trained language models based on big data, and
lightweight optimisation models using distillation techniques.
