Exploiting Linguistic Resources for Neural Machine TranslationUsing Multi-task Learning
Table 2: Impact of the training schedule in the machine translation task (BLEU/BEER/characTER)
['Systems', 'Default Schedule', 'Adapted Schedule', 'Adapted better']
The BLEU score improved by 0.4 BLEU points, BEER by 0.2 and characTER by 0.4. Furthermore, we compared each of the 36 measurements using the adapted schedule with the corresponding measurement using the default training schedule. Thus, the scores are calculated on same test set, based on the same metric. The model differs whether it is trained using the default or adapted training schedule. When directly comparing these systems, in 25 out of 36 cases the ones with the adapted schedule perform better. We analyzed the influence of the architecture as well as tasks considered in training in the same way. The influence of both aspects, however, was not as clear as the one from the training schedule. In order to get a deeper understanding, we analyzed in which cases it is more helpful to adapt the training schedule. As a first step, we looked at the correlation between the training schedule and the two different architectures.