10. A system for training a quality-prediction engine, the system comprising: a processor; a machine-translation engine stored in memory and executable by a processor to translate a document in a source language to a target language to obtain a machine-generated translation; a feature-comparison module stored in memory and executable by a processor to compare the machine-generated translation with a human-generated translation of the document, the human-generated translation in the target language; a mapping module stored in memory and executable by a processor to generate a mapping between features of the machine-generated translation and features of the human-generated translation based on the comparison, the mapping allowing determination of trust levels associated with translational accuracy of future machine-generated translations that lack corresponding human-generated translations; and a calibration module stored in memory and executable by a processor to calibrate the quality-prediction engine; wherein the calibration module: obtains a plurality of opinions for a plurality of sample translations generated by execution of the machine-translation engine, each of the opinions from a human and indicating a perceived trust level of corresponding sample translations; uses the quality-prediction engine to determine a trust level of each of the plurality of sample translations; determines a relationship between the plurality of opinions and the trust levels of the plurality of sample translations; and tunes the mapping to minimize any difference between the plurality of opinions and the trust levels of the plurality of sample translations.
13. The system of claim 10 , wherein the quality prediction engine includes an analysis module stored in memory and executable by a processor to analyze discrete units of the plurality of sample translations to determine a trust level for each discrete unit.