Conference Publication Details
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Sun Y., O'Brien S., O'Hagan M., Hollowood F.
EAMT 2010 - 14th Annual Conference of the European Association for Machine Translation
A novel statistical pre-processing model for Rule-Based Machine Translation system
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This paper introduces a new statistical preprocessing model for Rule-Based Machine Translation (RBMT) systems. We train a Statistical Machine Translation (SMT) system using monolingual corpora. This model can transform a source input to an RBMT system into a more target-language friendly or RBMTsystem friendly "pivot" language. We apply this proposed model to translation from English to Chinese in a pilot project. Automatic evaluation scores (BLEU, TER and GTM) show that this pre-processing model can increase the quality of the output of the RBMT system, especially with an increase in the size of the training corpus. This model is applicable to language pairs which differ in grammar and language structures.
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