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

Michael Denkowski, Chris Dyer, Alon Lavie
2014 Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics  
Using machine translation output as a starting point for human translation has become an increasingly common application 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)
more » ... dating the MT system's discriminative parameters with a MIRA step. Individually, these techniques can substantially improve MT quality, even over strong baselines. Moreover, we see super-additive improvements when all three techniques are used in tandem.
doi:10.3115/v1/e14-1042 dblp:conf/eacl/DenkowskiDL14 fatcat:fdyizdro7ffwhkx6vrl7biqm2i