@InProceedings{chatterjee-EtAl:2017:EACLlong,
  author    = {Chatterjee, Rajen  and  Gebremelak, Gebremedhen  and  Negri, Matteo  and  Turchi, Marco},
  title     = {Online Automatic Post-editing for MT in a Multi-Domain Translation Environment},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {525--535},
  abstract  = {Automatic post-editing (APE) for machine translation (MT) aims to fix recurrent
	errors made by the MT decoder by learning from correction examples. In
	controlled evaluation scenarios, the representativeness of the training set
	with respect to the test data is a key factor to achieve good performance.
	Real-life scenarios, however, do not guarantee such favorable learning
	conditions. Ideally, to be integrated in a real professional translation
	workflow (e.g. to play a role in computer-assisted translation framework), APE
	tools should be flexible enough to cope with continuous streams of diverse data
	coming from different domains/genres. To cope with this problem, we propose an
	online APE framework that is: i) robust to data diversity (i.e. capable to
	learn and apply correction rules in the right contexts) and ii) able to evolve
	over time (by continuously extending and refining its knowledge). In a
	comparative evaluation, with English-German test data coming in random order
	from two different domains, we show the effectiveness of our approach, which
	outperforms a strong batch system and the state of the art in online APE.},
  url       = {http://www.aclweb.org/anthology/E17-1050}
}