10. A method for training a machine translation system, comprising the steps of translating a test set from a first language to a second language using the 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 test set E; translating the test set E from a first language to a second language using a unidirectional translation corpus, so as to create set F in the second language; translating set F back to the first language using a unidirectional translation corpus so as to create set E′ in the first language; computing confidence scores for the translation of each item in the set based on the similarity of E and E′; creating a subset, H, of highest confidence scores; adding the translations in subset H directly to the parallel corpus without presenting the translations to a human translator for correction; creating a subset, L, of lowest confidence scores; presenting subset L to human translators for correction; and adding the human corrections to the parallel corpus.
14. The method of claim 10 wherein the translation accuracy score is measured using Bilingual Evaluation Understudy scoring metrics.