Evaluating the impact of using a domain-specific bilingual lexicon on the performance of a hybrid machine translation approach
Abstract
This paper describes an Example-Based Machine Translation prototype and presents an evaluation of the impact of using a domain specific vocabulary on its performance. This prototype is based on a hybrid approach which needs only monolingual texts in the target language and consists to combine translation candidates returned by a cross-language search engine with translation hypotheses provided by a finite-state transducer. The results of this combination are evaluated against a statistical language model of the target language in order to obtain the n-best translations. To measure the performance of this hybrid approach, we achieved several experiments using corpora on two domains from the European Parliament proceedings (Europarl) and the European Medicines Agency documents (Emea). The obtained results show that the proposed approach out performs the state-of-the-art Statistical Machine Translation system Moses when texts to translate are related to the specialized domain.
Keywords
Computational linguistics
Computer aided language translation
Learning algorithms
Natural language processing systems
Bilingual lexicons
European medicines agencies
Example based machine translations
Finite state transducers
Hybrid machine translation
Monolingual texts
Statistical language modeling
Translation systems
Translation (languages)