Paper: Predicting Success in Machine Translation

ACL ID D08-1078
Title Predicting Success in Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

The performance of machine translation sys- tems varies greatly depending on the source and target languages involved. Determining the contribution of different characteristics of language pairs on system performance is key to knowing what aspects of machine transla- tion to improve and which are irrelevant. This paper investigates the effect of different ex- planatory variables on the performance of a phrase-based system for 110 European lan- guage pairs. We show that three factors are strong predictors of performance in isolation: the amount of reordering, the morphological complexity of the target language and the his- torical relatedness of the two languages. To- gether, these factors contribute 75% to the variability of the performance of the system.