Paper: Adaptive Quality Estimation for Machine Translation

ACL ID P14-1067
Title Adaptive Quality Estimation for Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

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 diff...