@article{Kilian2012,
	abstract = {Research over the past years has shown that machine translation results can be greatly enhanced with the help of mono-or bilingual human contributors, eg by asking humans to proofread or correct outputs of machine translation systems. However, it remains difficult to determine the quality of individual revisions. This paper proposes a method to determine the quality of individual contributions by analyzing task-independent data. Examples of such data are completion time, number of keystrokes, etc. An initial evaluation showed promising F-measure values larger than 0.8 for support vector machine and decision tree based classifications of a combined test set of Vietnamese and German translations.},
	title = {Predicting Crowd-based Translation Quality with Language-independent Feature Vectors},
	author = {Kilian, Niklas and Krause, Markus and Runge, Nina and Smeddinck, Jan},
	year = {2012},
	papertype = {workshoppaper}
}