Learning from user feedback for machine translation in real-time

Abstract

Post-editing is the most popular approach to improve accuracy and speed of human translators by applying the machine translation (MT) technology. During the translation process, human translators generate the translation by correcting MT outputs in the postediting scenario. To avoid repeating the same MT errors, in this paper, we propose an efficient framework to update MT in real-time by learning from user feedback. This framework includes: (1) an anchor-based word alignment model, being specially designed to get correct alignments for unknown words and new translations of known words, for extracting the latest translation knowledge from user feedback; (2) an online translation model, being based on random forests (RFs), updating translation knowledge in real-time for later predictions and having a strong adaptability with temporal noise as well as context changes. The extensive experiments demonstrate that our proposed framework significantly improves translation quality as the number of feedback sentences increasing, and the translation quality is comparable to that of the off-line baseline system with all training data.