An Interactive Machine Translation System with Online Learning.
Daniel Ortiz-Martínez, Luis A. Leiva, Vicent Alabau, Ismael García-Varea and Francisco Casacuberta
In: ACL-HLT 2011(2011).

Abstract

State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Ma- chine Translation (IMT) framework, where the knowledge of a human translator is com- bined with the MT system. We present a sta- tistical IMT system able to learn from user feedback by means of the application of on- line learning techniques. These techniques al- low the MT 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 previ- ous IMT systems. Our IMT system is imple- mented in C++, JavaScript, and ActionScript; and is publicly available on the Web.

[Edit]