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Automatic Generation of Post-Editing Rule Sets from Parallel Corpora

Machine translations often have to be manually post-edited in order to meet quality demands. Since this is a labour-intensive task, it is desirable to find ways to automate (parts of) the post-editing process. This thesis presents a method for generation of rule sets for automatic post-editing of machine translations. The basic idea of the method is to generate a set of candidate rules from a parallel corpus of raw machine translations and manually post-edited translations, and to filter this set by measuring the effect of applying its individual rules to the raw machine translations of another corpus. As indicated by three frequently used evaluation metrics (BLEU, Meteor, and TER), rule sets created using this method are capable of increasing the translation quality of previously unseen translations from the same domain as the corpus used for filtering.