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.
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Conference papers
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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

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N. Semmar, O. Zennaki, M. Laib. Improving the performance of an example-based machine translation system using a domain-specific bilingual lexicon. 29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015, Oct 2015, Shangai, China. pp.106-115. ⟨cea-01844060⟩

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