EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only
Kamer Ali Yuksel Ahmet Gunduz Mohamed Al-Badrashiny Shreyas Sharma Hassan Sawaf
This paper presents EvolveMT for efficiently combining multiple machine
translation (MT) engines. The proposed system selects the output from a single
engine for each segment by utilizing online learning techniques to predict the
most suitable system for every translation request. A neural quality estimation
metric supervises the method without requiring reference translations. The
online learning capability of this system allows for dynamic adaptation to
alterations in the domain or machine translation engines, thereby obviating the
necessity for additional training. EvolveMT selects a subset of translation
engines to be called based on the source sentence features. The degree of
exploration is configurable according to the desired quality-cost trade-off.
Results from custom datasets demonstrate that EvolveMT achieves similar
translation accuracy at a lower cost than selecting the best translation of
each segment from all translations using an MT quality estimator. To our
knowledge, EvolveMT is the first meta MT system that adapts itself after
deployment to incoming translation requests from the production environment
without needing costly retraining on human feedback.