This paper presents a generalization technique that induces translation templates from given translation examples by replacing differing parts in these examples with typed variables. Since the type of each variable is also inferred during the learning process, each induced template is associated with a set of type constraints. The type constraints that are associated with a translation template restrict the usage of that translation template in certain contexts in order to avoid some of wrong translations. The types of variables are induced using the type lattices designed for both source language and target language. The proposed generalization technique has been implemented as a part of an EBMT system.

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