@InProceedings{scansani-EtAl:2017:HiT-IT,
  author    = {Scansani, Randy  and  Bernardini, Silvia  and  Ferraresi, Adriano  and  Gaspari, Federico  and  Soffritti, Marcello},
  title     = {Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources},
  booktitle = {Proceedings of the Workshop Human-Informed Translation and Interpreting Technology},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {Association for Computational Linguistics, Shoumen, Bulgaria},
  pages     = {1--10},
  abstract  = {This paper describes an approach to translating course unit descriptions from
	Italian and German into English, using a phrase-based machine translation (MT)
	system. The genre is very prominent among those requiring translation by
	universities in European countries in which English is a non-native language.
	For each language combination, an in-domain bilingual corpus including course
	unit and degree program descriptions is used to train an MT engine, whose
	output is then compared to a baseline engine trained on the Europarl corpus. In
	a subsequent experiment, a bilingual terminology database is added to the
	training sets in both engines and its impact on the output quality is evaluated
	based on BLEU and post-editing score. Results suggest that the use of
	domain-specific corpora boosts the engines quality for both language
	combinations, especially for German-English, whereas adding terminological
	resources does not seem to bring notable benefits.},
  url       = {https://doi.org/10.26615/978-954-452-042-7_001}
}