FP7-ICT Coordination and Support Action (CSA) QTLaunchPad (No. 296347) Preparation and Launch of a Large-scale Action for Quality Translation Technology Deliverable D2.2.1 Quality Estimation for System Selection and Combination Authors: Lucia Specia, Kashif Shah (University of Sheffield) Eleftherios Avramidis (DFKI) Ergun Bic ¸ici (DCU) Abstract. We present experiments using state of the art quality estimation models to improve the performance of machine translation systems without changing the internal functioning of such systems. The experiments include the following approaches: (i) n-best list re-ranking, where translation candidates (segments) produced by a machine translation system are re-ranked based on predicted quality scores such as to get the best translation ranked top; (ii) n-best list re- combination, where sub-segments from the n-best list are mixed using a lattice-based approach, and the complete generated segments are scored using quality predictions and then re-ranked as in (i); (iii) system selection, where translations produced by multiple machine translation systems and a human translator are sorted according to predicted quality to select the best translated seg- ment, including the challenging case where the source of the translation (i.e., which system/hu- man produced it) is unknown, and (iv) diagnosis of statistical machine translation systems by looking at internal features of the decoder and their correlation with translation quality, as well as using them to predict groups of errors in the translations. This project is funded by the European Union Copyright c  2014 University of Sheffield Project Final Revision Delivery Date Nature Reviewed By Web links Dissemination QTLaunchPad No. 296347 June 30, 2014 February 28, 2014 Deliverable Josef van Genabith (DCU) http://www.qt21.eu/launchpad/ Public This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).