Prediction of Translation Techniques for the Translation Process
Fan Zhou Vincent Vandeghinste
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%.