aids, and discuss its application to MT evaluation.

A classification system for errors in machine translation (MT) output is presented. Translation errors are assigned to categories to provide a systematic basis for comparing the translations produced by competing MT systems. The classification system is designed for use by potential MT users

This report describes experiments performed during preparations for the NIST 2009 MT evaluation, where we participated in the con-strained Chinese-English condition with PORTAGE. The aim is to publicize new findings about the optimum configuration, and to sug-

This paper describes the automatic MT evaluation

shared task within the ACL WMT-07 workshop. This paper recaps the technical details underlying the metric and describes recent improvements in the metric. The latest release includes improved metric parameters and extends the metric to support evaluation of MT output in Spanish, French and German

Abstract not found

Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused.

We introduce the first fully automatic, fully semantic frame based MT evaluation metric, MEANT, that outperforms all other commonly used automatic metrics in correlating with human judgment on translation adequacy. Recent work on HMEANT, which is a human metric, indicates that machine translation

In this paper we present a novel method for deriving paraphrases during automatic MT evaluation using only the source and reference texts, which are necessary for the evaluation, and word and phrase alignment software. Using target language paraphrases produced through word and phrase alignment a