Confidence-based Active Learning Methods for Machine Translation

摘要

The paper presents experiments with active learning methods for the acquisition of training data in the context of machine translation. We propose a confidence-based method which is superior to the state-of-the-art method both in terms of quality and complexity. Additionally, we discovered that oracle selection techniques that use real quality scores lead to poor results, making the effectiveness of confidence-driven methods of active learning for machine translation questionable.