Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources

Randy Scansani, Silvia Bernardini, Adriano Ferraresi, Federico Gaspari, Marcello Soffritti

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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.
Anthology ID:
W17-7901
Volume:
Proceedings of the Workshop Human-Informed Translation and Interpreting Technology
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Irina Temnikova, Constantin Orasan, Gloria Corpas Pastor, Stephan Vogel
Venue:
RANLP
SIG:
Publisher:
Association for Computational Linguistics, Shoumen, Bulgaria
Note:
Pages:
1–10
Language:
URL:
https://doi.org/10.26615/978-954-452-042-7_001
DOI:
10.26615/978-954-452-042-7_001
Bibkey:
Cite (ACL):
Randy Scansani, Silvia Bernardini, Adriano Ferraresi, Federico Gaspari, and Marcello Soffritti. 2017. Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources. In Proceedings of the Workshop Human-Informed Translation and Interpreting Technology, pages 1–10, Varna, Bulgaria. Association for Computational Linguistics, Shoumen, Bulgaria.
Cite (Informal):
Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources (Scansani et al., RANLP 2017)
Copy Citation:
PDF:
https://doi.org/10.26615/978-954-452-042-7_001