Improved Search Strategy for Interactive Predictions in Computer-Assisted Translation

The statistical machine translation outputs are not error-free and in a high quality yet. So in the cases that we need high quality translations we definitely need the human intervention. An interactive-predictive machine translation is a framework, which enables the collaboration of the human and the translation system. Here, we address the problem of searching the best suffix to propose to the user in the phrase-based interactive prediction scenario. By adding the jump operation to the common edit distance based search, we try to overcome the lack of some of the reorderings in the search graph which might be desired by the user. The experiments results shows that this method improves the base method by 1.35% in KSMR, and if we combine the edit error in the proposed method with the translation scores given by the statistical models to select the offered suffix, we could gain the KSMR improvement of about 1.63% compared to the base search method.