Transformation-Based Correction of Rule-Based MT

We present a pilot study for using transformation-based learning for automatic correction of rule-based machine translation. Correction rules are learned based on a parallel corpus of machine translations from a commercial machine translation system and a human-corrected version of these translations. The correction rules exploit information on word forms and part of speech. The experiment results in a relative increase in translation quality of 4.6% measured using the BLEU metric.