@InProceedings{pal-naskar-vangenabith:2016:COLING,
  author    = {Pal, Santanu  and  Naskar, Sudip Kumar  and  van Genabith, Josef},
  title     = {Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2559--2570},
  abstract  = {In this paper we combine two strands of machine translation (MT) research:
	automatic post-editing (APE) and multi-engine (system combination) MT. APE
	systems learn a target-language-side second stage MT system from the data
	produced by human corrected output of a first stage MT system, to improve the
	output of the first stage MT in what is essentially a sequential MT system
	combination architecture. At the same time, there is a rich research literature
	on parallel MT system combination where the same input is fed to multiple
	engines and the best output is selected or smaller sections of the outputs are
	combined to obtain improved translation output. In the paper we show that
	parallel system combination in the APE stage of a sequential MT-APE combination
	yields substantial translation improvements both measured in terms of automatic
	evaluation metrics as well as in terms of productivity improvements measured in
	a post-editing experiment. We also show that system combination on the level of
	APE alignments yields further improvements. Overall our APE system yields
	statistically significant improvement of 5.9% relative BLEU over a strong
	baseline (English--Italian Google MT) and 21.76% productivity increase in a
	human post-editing experiment with professional translators.},
  url       = {http://aclweb.org/anthology/C16-1241}
}