A re-examination on features in regression based approach to automatic MT evaluation
Shuqi Sun, Yin Chen, Jufeng Li · 2008
Machine learning methods have been extensively employed in developing MT evaluation metrics and several studies show that it can help to achieve a better correlation with human assessments. Adopting the regression SVM framework, this paper discusses the linguistic motivated feature formulation strategy. We argue that "blind" combination of available features does not yield a general metrics with high correlation rate with human assessments. Instead, certain simple intuitive features serve better in establishing the regression SVM evaluation model. With six features selected, we show evidences to support our view through a few experiments in this paper.