Improving the performance of an example-based machine translation system using a domain-specific bilingual lexicon

N. Semmar; O. Zennaki; M. Laib

Improving the performance of an example-based machine translation system using a domain-specific bilingual lexicon

N. Semmar ¹ O. Zennaki ¹ M. Laib ¹

1 DIASI - Département Intelligence Ambiante et Systèmes Interactifs

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

Document type :

Conference papers

Domain :

Computer Science [cs]

Complete list of metadatas

https://hal-cea.archives-ouvertes.fr/cea-01844060
Contributor : Léna Le Roy <>
Submitted on : Thursday, July 19, 2018 - 10:15:01 AM
Last modification on : Wednesday, January 23, 2019 - 2:39:26 PM

Improving the performance of an example-based machine translation system using a domain-specific bilingual lexicon

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Improving the performance of an example-based machine translation system using a domain-specific bilingual lexicon

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