Paper: Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation

ACL ID E14-1042
Title Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Using machine translation output as a starting point for human translation has become an increasingly common applica- tion of MT. We propose and evaluate three computationally efficient online methods for updating statistical MT systems in a scenario where post-edited MT output is constantly being returned to the system: (1) adding new rules to the translation model from the post-edited content, (2) updating a Bayesian language model of the target language that is used by the MT system, and (3) updating the MT system?s discriminative parameters with a MIRA step. Individually, these tech- niques can substantially improve MT qual- ity, even over strong baselines. Moreover, we see super-additive improvements when all three techniques are used in tandem.