Error Selection Methods for Machine Translation Error Analysis

Abstract

Error analysis is used to improve accuracy of machine translation (MT) systems. Various methods of analyzing MT errors have been proposed; however, most of these methods are based on differences between translations and references that are translated independently by human translators, and few methods have been proposed for manual error analysis. This work proposes a method that uses a machine learning framework to identify errors in MT output, and improves efficiency of manual error analysis. Our method builds models that classify low and high quality translations, then identifies features of low quality translations to improve efficiency of the manual analysis. Experiments showed that by using our methods, we could improve the efficiency of MT error analysis.