Example Based Machine Translation

Abstract

EBMT was most notably attributed to Nagao and his famous translation by Analogy paper in 1984. EBMT retrieves similar examples (pairs of source phrases, sentences , or texts and their translations) from a database examples, adapting the examples to translate new input. Based on the intuition that humans make use of previously seen translation examples to translate unseen input. The EBMT system consists of two databases: An example database and a thesaurus and also three translation modules: analysis, example-based transfer, and generation. Translation performed using database of examples extracted from corpora. There are four stages of work in EBMT, namely, example acquisition, example base management, example application and target sentence synthesis.The underlying principle for EBMT is as simple as this: remember everything translated in the past and use everything available to facilitate the translation of the next utterance. We know computers are the most fantastic machines to memorize such things as text pairs and their frequencies, and we thus have reason to believe that EBMT represents the MT approach with the greatest potential. It opens a wide area of research opportunities.