Example based machine translation is a translation technique that leans on machine learning paradigm. This technique had been modeled by the learning process as: a man is given short and simple sentences in language A with their correspondences in language B; he memorizes these pairs and then becomes able to translate new sentences via these pairs in the memory. In our system the translation pairs are kept as translation templates. A translation template is induced from given two translation examples by replacing differing parts in these examples by variables. A variable replacing a difference that consists of two differing parts (one from the first example, and the other one from the second example) is a generalization of those two differing parts and these variables are supported with part-of-speech tag information in order to deteriorate incorrect translations. After the learning phase, translation is achieved by finding the appropriate template(s) and replacing the variables.