Ordering Translation Templates by Assigning Confidence Factors

TTL Translation Template Learner algorithm learns lexical level correspondences between two translation examples by using analog ical reasoning The sentences used as translation examples have similar and di erent parts in the source language which must correspond to the similar and di erent parts in the target language Therefore these corre spondences are learned as translation templates The learned translation templates are used in the translation of other sentences However we need to assign con dence factors to these translation templates to order translation results with respect to previously assigned con dence factors This paper proposes a method for assigning con dence factors to trans lation templates learned by the TTL algorithm Training data is used for collecting statistical information that will be used in con dence factor as signment process In this process each template is assigned a con dence factor according to the statistical information obtained from training data Furthermore some template combinations are also assigned con dence factors in order to eliminate certain combinations resulting bad translation