@InProceedings{born-mesgar-strube:2017:DiscoMT,
  author    = {Born, Leo  and  Mesgar, Mohsen  and  Strube, Michael},
  title     = {Using a Graph-based Coherence Model in Document-Level Machine Translation},
  booktitle = {Proceedings of the Third Workshop on Discourse in Machine Translation},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {26--35},
  abstract  = {Although coherence is an important aspect of any text generation system, it has
	received little attention in the context of machine translation (MT) so far. 
	We hypothesize that the quality of document-level translation can be improved
	if MT models take into account the semantic relations among sentences during
	translation. We integrate the graph-based coherence model proposed by Mesgar
	and Strube, (2016) with Docent (Hardmeier et al., 2012, Hardmeier, 2014) a
	document-level machine translation system. The application of this graph-based
	coherence modeling approach is novel in the context of machine translation. We
	evaluate the coherence model and its effects on the quality of the machine
	translation. The result of our experiments shows that our coherence model
	slightly improves the quality of translation in terms of the average Meteor
	score.},
  url       = {http://aclweb.org/anthology/W17-4803}
}