Potential for Improvement of Sentence Translations

Natural language processing tasks like machine translation can be parallelized, corrected, and repeated at the sentence level since they are data-centric and highly parallel. Each sentence's translation quality can vary, thus any problems must be fixed on an individual basis. At the same time, translation quality should be predicted before further processing and shipment since we cannot be sure without knowing the target language. We identify the potential for improvement (pfi) of sentence translations, whether a translation has potential to be improved by further processing or retranslation, and achieve overall performance improvements. We describe a method for ranking the obtained translations to fix translation errors by retranslating the sentences using a computationally more demanding translation model. In the first step we identify the potential regardless of prediction errors and later we use triangular uninorm combination to sort instances in our decision making process about sorting sentences for retranslation. By retranslating the top 200 sentences that have the most pfi we achieve up to 2.31 BLEU improvements.