Cost-sensitive active learning for computer-assisted translation
We present a new active learning framework for computed assisted translation. Our goal is to make the translation process as efficient as possible for human translators. We implement efficient techniques to detect informative sentences. We use online learning techniques to update the translation model with user feedback. Results show that our method allows to double the productivity of conventional approaches.
active learning computer-assisted translation online learning interactive machine translation