The basic idea of Example-Based Machine Translation (EBMT) is to reuse examples of already existing translations as the basis for for new translation. The process of EBMT is broken down into three stages:
The matching stage in example based machine translation finds examples that are going to contribute to the translation on the basis of their similarity with the input. The way matching stage should be implemented is based on how the examples are stored. In old systems, examples were stored as annotated tree structures and the constituents in the two languages were connected by explicit links. The input to be matched is parsed using the grammar that was used to build the example database and the tree is formed. This tree is compared with trees in the example database.
The input and examples can be matched by comparing character by character. This process is called sequence comparison. Alignment and recombination will be difficult if this approach is used. Examples may be annotated with Parts-Of-Speech tags. Several simple examples may be combined into a more general single example containing variables. The examples should be analyzed to see if they are suitable for further processing. Overlapping or contradictory examples should be properly dealt with.
Alignment is used to identify which parts of the corresponding translation are to be reused. Alignment is done by using bilingual dictionary or comparing with other examples. The process of alignment in example based machine translation must be automated.
Recombination is the final phase in example based machine translation approach. Recombination makes sure that the reusable parts in example identified during alignement are putting together in a legitimate way. It takes source language sentences and a set of translation patters as inputs and produces target language sentences as outputs. The design of recombination strategy depends on previous matching and alignment phases.
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