DFKI-LT - QuEst — Design, Implementation and Extensions of a Framework for Machine Translation Quality Estimation

Kashif Shah, Eleftherios Avramidis, Ergun Bišici, Lucia Specia
QuEst — Design, Implementation and Extensions of a Framework for Machine Translation Quality Estimation
in: Eva Hajičovß (ed.):
1 The Prague Bulletin of Mathematical Linguistics volume 100, Pages 19-30, Charles University in Prague, Prague, Czech Republic, 9/2013
 
In this paper we present QuEst, an open source framework for machine translation quality estimation. The framework includes a feature extraction component and a machine learning component. We describe the architecture of the system and its use, focusing on the feature extraction component and on how to add new feature extractors. We also include experiments with features and learning algorithms available in the framework using the dataset of the WMT13 Quality Estimation shared task.
 
Files: BibTeX, art-shah-avramidis-bicici-specia.pdf