@inproceedings{scansani-etal-2017-enhancing,
    title = "Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources",
    author = "Scansani, Randy  and
      Bernardini, Silvia  and
      Ferraresi, Adriano  and
      Gaspari, Federico  and
      Soffritti, Marcello",
    editor = "Temnikova, Irina  and
      Orasan, Constantin  and
      Pastor, Gloria Corpas  and
      Vogel, Stephan",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_001",
    doi = "10.26615/978-954-452-042-7_001",
    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.",
}