Example-based machine translation
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Example-based machine translation (EBMT) approach is often characterised by its use of a bilingual corpus as its main knowledge base, at run-time. It is essentially a translation by analogy and can be viewed as an implementation of case-based reasoning approach of machine learning.
For example, if we have been trained using some text containing the sentences: President Kennedy was shot dead during the parade. and The convict escaped on July 15th. We could translate the sentence The convict was shot dead during the parade. by substituting the appropriate parts of the sentences.
This translation technique does not always cover the entire language, so it is sometimes used as a component in a hybrid system.
First suggested by Nagao Makoto (http://www-nagao.kuee.kyoto-u.ac.jp/member/nagao) in 1984, it soon attracted the attention of scientists in the field of natural language processing.
English | Japanese |
| How much is that red umbrella? | Ano akai kasa wa ikura desu ka. |
| How much is that small camera? | Ano chiisai kamera wa ikura desu ka. |