Personalized Machine Translation: Preserving Original Author Traits
Table 6: TED en-fr classification scores (%).
['dataset', 'precision M', 'precision F', 'recall M', 'recall F', 'acc.']
The classification accuracy of English originals is 80.4%. We attribute this difference to the more emotional and personal nature of TED speeches, compared with the formal language of the EP proceedings. Both personalized SMT models significantly outperform their baseline counterpart, as well as the manual translation, yielding 77.2% and 77.7% accuracy for MT-P1 and MT-P2, respectively.