A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation
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)
doi:10.3115/v1/e14-1042
dblp:conf/eacl/DenkowskiDL14
fatcat:fdyizdro7ffwhkx6vrl7biqm2i