A Machine Learning-Based Evaluation Method for Machine Translation

Constructing a classifier that distinguishes machine translations from human translations is a promising approach to automatic evaluation of machine-translated sentences. Using this approach, we constructed a classifier using Support Vector Machines based on word-alignment distributions between source sentences and human or machine translations. This paper investigates the validity of the classification-based method by comparing it with well-known evaluation methods. The experimental results show that our classification-based method can accurately evaluate fluency of machine translations.