Dimensionality reduction methods for machine translation quality estimation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-013-9139-3[EN] Quality estimation (QE) for machine translation is usually addressed as a regression problem where a learning model is used to predict a quality score from a (usually highly-redundant) set of features that represent the translation. This redundancy hinders model learning, and thus penalizes the performance of quality estimation systems. We propose different dimensionality reduction methods based on partial least squares regression to overcome this problem, and compare them against several reduction methods previously used in the QE literature. Moreover, we study how the use of such methods influence the performance of different learning models. Experiments carried out on the English-Spanish WMT12 QE task showed that it is possible to improve prediction accuracy while significantly reducing the size of the feature sets.This work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat project (grants agreement no. 287576), by Spanish MICINN under TIASA (TIN2009-14205-C04-02) project, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).González Rubio, J.; Navarro Cerdán, JR.; Casacuberta Nolla, F. (2013). Dimensionality reduction methods for machine translation quality estimation. Machine Translation. 27(3-4):281-301. https://doi.org/10.1007/s10590-013-9139-3S281301273-4Amaldi E, Kann V (1998) On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor Comput Sci 209(1–2):237–260Anderson TW (1958) An introduction to multivariate statistical analysis. 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Second report on dissemination activities

Workpackage 7 comprises of dissemination activities of the casmacat project. In this report, we summarize the promotion of project goals, progress and outcomes to the larger academic research community, the commercial sector targeted by the work, and beyond