Assessing the Use of Terminology in Phrase-Based Statistical Machine Translation for Academic Course Catalogue Translation

In this contribution we describe an approach to evaluate the use of terminology in a phrase-based machine translation system to translate course unit descriptions from Italian into English. The genre is very prominent among those requiring translation by universities in European countries where English is not a native language. Two MT engines are trained on an in-domain bilingual corpus and a subset of the Europarl corpus, and one of them is enhanced adding a bilingual termbase to its training data. Overall systems’ performance is assessed through the BLEU score, whereas the f-score is used to focus the evaluation on term translation. Furthermore, a manual analysis of the terms is carried out. Results suggest that in some cases - despite the simplistic approach implemented to inject terms into the MT system - the termbase was able to bias the word choice of the engine.