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An Empirical Study on Computing Consensus Translations from Multiple Machine Translation Systems

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

Abstract

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.

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