Adaptive Quality Estimation for Machine Translation

The automatic estimation of machine translation (MT) output quality is a hard task in which the selection of the appropriate algorithm and the most predictive features over reasonably sized training sets plays a crucial role. When moving from controlled lab evaluations to real-life scenarios the task becomes even harder. For current MT quality estimation (QE) systems, additional complexity comes from the difficulty to model user and domain changes. Indeed, the instability of the systems with respect to data coming from different distributions calls for adaptive solutions that react to new operating conditions. To tackle this issue we propose an online framework for adaptive QE that targets reactivity and robustness to user and domain changes. Contrastive experiments in different testing conditions involving user and domain changes demonstrate the effectiveness of our approach.