Real-life translation quality estimation for MT system selection
Tipo de documentoComunicación de congreso
Fecha de publicación2013
Condiciones de accesoAcceso abierto
Research on translation quality annotation and estimation usually makes use of standard language, sometimes related to a specific language genre or domain. However, real-life machine translation (MT), performed for instance by on-line translation services, has to cope with some extra dif- ficulties related to the usage of open, non-standard and noisy language. In this paper we study the learning of quality estimation (QE) models able to rank translations from real-life input according to their goodness without the need of translation references. For that, we work with a corpus collected from the 24/7 Reverso.net MT service, translated by 5 different MT systems, and manually annotated with quality scores. We define several families of features and train QE predictors in the form of regressors or direct rankers. The predictors show a remarkable correlation with gold standard rankings and prove to be useful in a system combination scenario, obtaining better results than any individual translation system.
CitaciónFormiga, L.; Marquez, L.; Pujantell, J. Real-life translation quality estimation for MT system selection. A: Machine Translation Summit. "Proceedings of Machine Translation Summit XIV". Niça: 2013, p. 69-76.