We describe our submissions to the WMT11 shared MT evaluation task: MTeRater and MTeRater-Plus. Both are machine-learned metrics that use features from e-rater R ○ , an automated essay scoring engine designed to assess writing proficiency. Despite using only features from e-rater and without comparing to translations, MTeRater achieves a sentencelevel correlation with human rankings equivalent to BLEU. Since MTeRater only assesses fluency, we build a meta-metric, MTeRater-Plus, that incorporates adequacy by combining MTeRater with other MT evaluation metrics and heuristics. This meta-metric has a higher correlation with human rankings than either MTeRater or individual MT metrics alone. However, we also find that e-rater features may not have significant impact on correlation in every case.
