Exploiting Linguistic Resources for Neural Machine TranslationUsing Multi-task Learning
Table 1: Results of multi-task learning architectures on the machine translation task (BLEU/BEER/characTER)
['Task(s)', 'Arch.', 'Valid dev 2010', 'Test tst2013', 'Test tst2014']
The table displays the performance on the validation set and on both test sets. For all experiments, we first show the BLEU score, then the BEER score and finally the characTER. This model improved the performance by 1.5 BLEU points over the baseline system. Also the BEER score is improved by 1 and the characTER score reduced by 1.8 to 2 points.