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

Kashif Shah; Eleftherios Avramidis; Ergun Biçici; Lucia Specia

In: Eva Hajičová (Hrsg.). The Prague Bulletin of Mathematical Linguistics (PBML), Vol. 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.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence