Czech-English dependency-based machine translation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Automatic construction of machine translation knowledge using translation literalness
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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In this paper we report on the results of an experiment in designing resource-light metrics that predict the potential translation complexity of a text or a corpus of homogenous texts for state-of-the-art MT systems. We show that the best prediction of translation complexity is given by the average number of syllables per word (ASW). The translation complexity metrics based on this parameter are used to normalise automated MT evaluation scores such as BLEU, which otherwise are variable across texts of different types. The suggested approach makes a fairer comparison between the MT systems evaluated on different corpora. The translation complexity metric was integrated into two automated MT evaluation packages - BLEU and the Weighted N-gram model. The extended MT evaluation tools are available from the first author's web site: http://www.comp.leeds.ac.uk/bogdan/evalMT.html