Translator2Vec: Understanding and Representing Human Post-Editors
Table 4: Results of model and baselines for editor identification. Reported are average test set accuracies of 5 runs, with 7 editors for En-De and 6 editors for En-Fr.
['[EMPTY]', 'En-De (%)', 'En-Fr (%)']
Among the baselines, we observe a gradual improvement as models have access to more information. The fact that the MT baseline performs closely to the random baseline is reassuring, showing that there is no bias in the type of text that each editor receives. As expected, the dual encoder model with attention, being able to attend to each word of the MT and post-edited text , is the one which performs the best, surpassing the random baseline by a large margin. However, none of these baselines have a satisfactory performance on the editor identification task.