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.
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. 15. The method of claim 10 wherein the confidence scores are computed using at least one of Bilingual Evaluation Understudy or Translation Edit Rate scoring metrics.