An Empirical Study on Computing Consensus Translations from Multiple Machine Translation
Proceedings of the 2007 Joint Conference on Empirical
Methods in Natural Language Processing and Computational Natural Language Learning
(EMNLP-CoNLL), Association for Computational Linguistics, 209 N. Eighth
Street, East Stroudsburg, PA, USA, pp. 986-995
This paper presents an empirical study on how different selections of input
translation systems affect translation quality in system combination. We give
empirical evidence that the systems to be combined should be of similar quality and
need to be almost uncorrelated in order to be beneficial for system combination.
Experimental results are presented for composite translations computed from large
numbers of different research systems as well as a set of translation systems
derived from one of the best-ranked machine translation engines in the 2006 NIST
machine translation evaluation.