An Active Learning Scenario for Interactive Machine Translation
This paper provides the first experimental study of an active learn- ing (AL) scenario for interactive machine translation (IMT). Unlike other IMT implementations where user feedback is used only to im- prove 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 strat- egy to select the sentences that are worth to be interactively trans- lated, 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 se- vere time constraints inherent to the IMT framework. Experiments in a simulated setting showed that the use of AL dramatically re- duces user effort required to obtain translations of a given quality.