9. A method for training a machine translation system, comprising the steps of: translating a test set from a first collection to a second collection using an existing parallel corpus stored in memory on the machine translation system; calculating a translation accuracy score for each item in the test set; comparing the translation accuracy score for each item to a desired performance score to determine whether the parallel corpus needs to be updated for that item; if the translation accuracy score for an item is equal to or greater than a desired performance score, removing that item from the test set, so as to create a test set E; using a unidirectional translation corpus, translating the test set E from the first collection to the second collection so as to create a set F in the second collection and translating the set F from the second collection back to the first collection so as to create a set E′ in the first collection, wherein differences between E and E′ are determined; computing confidence scores for a translation of each item in the test set E based on a similarity of E and E′; and adding translations to the parallel corpus based on the confidence scores.
13. The method of claim 9 , further comprising the step of presenting a Graphical User Interface (GUI) to a user, the GUI displaying at least controls for changing features to compute the confidence scores, the features being at least one of a scoring metric and values used to create two subsets, one subset comprising highest confidence scores computed and the other subset comprising lowest confidence scores computed.