Online optimisation of log-linear weights in interactive machine translation

Mara Chinea-Rios, Germán Sanchis-Trilles, Daniel Ortiz-Martínez, Francisco Casacuberta

Abstract: Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In such a setup, it is crucial that the system is able to learn from the errors that have already been corrected. In this paper, we analyse the applicability of discriminative ridge regression for learning the log-linear weights of a state-of-the-art machine translation system underlying an interactive machine translation framework, with encouraging results.