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 appro- priate algorithm and the most predictive features over reasonably sized training sets plays a crucial role. When moving from controlled lab evaluations to real-life sce- narios the task becomes even harder. For current MT quality estimation (QE) sys- tems, additional complexity comes from the difficulty to model user and domain changes. Indeed, the instability of the sys- tems with respect to data coming from dif- ferent distributions calls for adaptive so- lutions that react to new operating con- ditions. To tackle this issue we propose an online framework for adaptive QE that targets reactivity and robustness to user and domain changes. Contrastive exper- iments in different testing conditions in- volving user and domain changes demon- strate the effectiveness of our approach.