Example-based machine translation

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 in 1984, it soon attracted the attention of scientists in the field of natural language processing.

Example of bilingual corpus

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.

Related topics

• Machine translation
• Machine learning
• Translation memory