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Daniel Ortiz-Martínez, Luis A. Leiva, Vicent Alabau, Ismael García-Varea, Francisco Casacuberta. An Interactive Machine Translation System with Online Learning. Proceedings of the ACL-HLT 2011 System Demonstrations, 2011. pp. 68-73. Association for Computational Linguistics.

State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Machine Translation (IMT) framework, where the knowledge of a human translator is combined with the MT system. We present a statistical IMT system able to learn from user feedback by means of the application of online learning techniques. These techniques allow theMT system to update the parameters of the underlying models in real time. According to empirical results, our system outperforms the results of conventional IMT systems. To the best of our knowledge, this online learning capability has never been provided by previous IMT systems. Our IMT system is implemented in C++, JavaScript, and ActionScript; and is publicly available on the Web.