Online adaptation to post-edits for phrase-based statistical machine translation

Recent research has shown that accuracy and speed of human translators can benefit from post-editing output of machine translation systems, with larger benefits for higher quality output. We present an efficient online learning framework for adapting all modules of a phrase-based statistical machine translation system to post-edited translations. We use a constrained search technique to extract new phrase-translations from post-edits without the need of re-alignments, and to extract phrase pair features for discriminative training without the need for surrogate references. In addition, a cache-based language model is built on $$n$$n-grams extracted from post-edits. We present experimental results in a simulated post-editing scenario and on field-test data. Each individual module substantially improves translation quality. The modules can be implemented efficiently and allow for a straightforward stacking, yielding significant additive improvements on several translation directions and domains.