1. A method for training a quality-prediction engine, the method comprising: translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation; comparing the machine-generated translation with a human-generated translation of the document, the human-generated translation in the target language; generating 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 calibrating the quality prediction engine, wherein calibrating the quality-prediction engine includes: obtaining 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; using the quality-prediction engine to determine a trust level of each of the plurality of sample translations; determining a relationship between the plurality of opinions and the trust levels of the plurality of sample translations; and tuning the mapping to minimize any difference between the plurality of opinions and the trust levels of the plurality of sample translations.
1. A method for training a quality-prediction engine, the method comprising: translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation; comparing the machine-generated translation with a human-generated translation of the document, the human-generated translation in the target language; generating 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 calibrating the quality prediction engine, wherein calibrating the quality-prediction engine includes: obtaining 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; using the quality-prediction engine to determine a trust level of each of the plurality of sample translations; determining a relationship between the plurality of opinions and the trust levels of the plurality of sample translations; and tuning the mapping to minimize any difference between the plurality of opinions and the trust levels of the plurality of sample translations. 2. The method of claim 1 , wherein the translational accuracy represents an alignment-estimation between the machine-generated translation and a prospective human-generated translation.