2Universidade Nova de Lisboa

This paper reports the first results of an on-going research on evaluation of Machine Translation quality. The starting point for this work was the framework of ISLE (the International Standards for Language Engineering), which provides a classification for evaluation of Machine Translation. In order to make a quantitative evaluation of translation quality, we pursue a more consistent, fine-grained and comprehensive classification of possible translation errors and we propose metrics for sentence level errors, specificall