@InProceedings{mascarell:2017:DiscoMT,
  author    = {Mascarell, Laura},
  title     = {Lexical Chains meet Word Embeddings in Document-level Statistical 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     = {99--109},
  abstract  = {Currently under review for EMNLP 2017
	The phrase-based Statistical Machine Translation (SMT) approach deals with
	sentences in isolation, making it difficult to consider discourse context in
	translation. This poses a challenge for ambiguous words that need discourse
	knowledge to be correctly translated. We propose a method that benefits from
	the semantic similarity in lexical chains to improve SMT output by integrating
	it in a document-level decoder. We focus on word embeddings to deal with the
	lexical chains, contrary to the traditional approach that uses lexical
	resources. Experimental results on German-to-English show that our method
	produces correct translations in up to 88% of the changes, improving the
	translation in 36%-48% of them over the baseline.},
  url       = {http://aclweb.org/anthology/W17-4813}
}