An active learning scenario for interactive machine translation

Abstract

This paper provides the first experimental study of an active learning (AL) scenario for interactive machine translation (IMT). Unlike other IMT implementations where user feedback is used only to improve the predictions of the system, our IMT implementation takes advantage of user feedback to update the statistical models involved in the translation process. We introduce a sentence sampling strategy to select the sentences that are worth to be interactively translated, and a retraining method to update the statistical models with the user-validated translations. Both, the sampling strategy and the retraining process are designed to work in real-time to meet the severe time constraints inherent to the IMT framework. Experiments in a simulated setting showed that the use of AL dramatically reduces user effort required to obtain translations of a given quality.