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Conference Papers Year : 2015

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

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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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Dates and versions

cea-01844060 , version 1 (19-07-2018)

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  • HAL Id : cea-01844060 , version 1

Cite

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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