 |
Conference papers
Improving the performance of an example-based machine translation system using a domain-specific bilingual lexicon
Abstract : In this paper, we study the impact of using a domain-specific bilingual lexicon on the performance of an Example-Based Machine Translation system. We conducted experiments for the English-French language pair on in-domain texts from Europarl (European Parliament Proceedings) and out-of-domain texts from Emea (European Medicines Agency Documents), and we compared the results of the Example-Based Machine Translation system against those of the Statistical Machine Translation system Moses. The obtained results revealed that adding a domain-specific bilingual lexicon (extracted from a parallel domain-specific corpus) to the general-purpose bilingual lexicon of the Example-Based Machine Translation system improves translation quality for both in-domain as well as outof-domain texts, and the Example-Based Machine Translation system outperforms Moses when texts to translate are related to the specific domain.
Keywords :
Natural language processing systems
Bilingual lexicons
Domain specific
European medicines agencies
European Parliament
Example based machine translations
Language pairs
Statistical machine translation system
Translation quality
Translation (languages)
Medicine
Computer aided language translation
Computational linguistics
https://hal-cea.archives-ouvertes.fr/cea-01844060
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 19, 2018 - 10:15:01 AM
Last modification on : Friday, June 25, 2021 - 9:48:06 AM
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 19, 2018 - 10:15:01 AM
Last modification on : Friday, June 25, 2021 - 9:48:06 AM
`