Abstract
The performance of machine translation systems 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 translation to improve and which are irrelevant. This paper investigates the effect of different explanatory variables on the performance of a phrase-based system for 110 European language 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 historical relatedness of the two languages. Together, these factors contribute 75% to the variability of the performance of the system. © 2008 Association for Computational Linguistics.
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CITATION STYLE
Birch, A., Osborne, M., & Koehn, P. (2008). Predicting success in machine translation. In EMNLP 2008 - 2008 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference: A Meeting of SIGDAT, a Special Interest Group of the ACL (pp. 745–754). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1613715.1613809
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