Prediction of Translation Techniques for the Translation Process

Abstract

Machine translation (MT) encompasses a variety of methodologies aimed at enhancing the accuracy of translations. In contrast, the process of human-generated translation relies on a wide range of translation techniques, which are crucial for ensuring linguistic adequacy and fluency. This study suggests that these translation techniques could further optimize machine translation if they are automatically identified before being applied to guide the translation process effectively. The study differentiates between two scenarios of the translation process: from-scratch translation and post-editing. For each scenario, a specific set of experiments has been designed to forecast the most appropriate translation techniques. The findings indicate that the predictive accuracy for from-scratch translation reaches 82%, while the post-editing process exhibits even greater potential, achieving an accuracy rate of 93%.