Online learning for interactive statistical machine translation

State-of-the-art Machine Translation (MT) systems are still far from being perfect. An al-ternative is the so-called Interactive Machine Translation (IMT) framework. In this frame-work, the knowledge of a human translator is combined with a MT system. The vast ma-jority of the existing work on IMT makes use of the well-known batch learning paradigm. In the batch learning paradigm, the training of the IMT system and the interactive translation process are carried out in separate stages. This paradigm is not able to take advantage of the new knowledge produced by the user of the IMT system. In this paper, we present an ap-plication of the online learning paradigm to the IMT framework. In the online learning paradigm, the training and prediction stages are no longer separated. This feature is par-ticularly useful in IMT since it allows the user feedback to be taken into account. The online learning techniques proposed here incremen-tally update the statistical models involved in the translation process. Empirical results show the great potential of online learning in the IMT framework.