Using Monolingual Data in Neural Machine Translation:a Systematic Study
Table 2: Performance wrt. different BT qualities
['[BOLD] English→French', '[BOLD] English→French  [BOLD] test-07', '[BOLD] English→French  [BOLD] test-07', '[BOLD] English→French  [BOLD] test-07', '[BOLD] English→French  [BOLD] test-08', '[BOLD] English→French  [BOLD] test-08', '[BOLD] English→French  [BOLD] test-08', '[BOLD] English→French  [BOLD] newstest-14', '[BOLD] English→French  [BOLD] newstest-14', '[BOLD] English→French  [BOLD] newstest-14']
(higher is better) and characTER Wang et al. (smaller is better). As they are trained from much smaller amounts of data than current systems, these baselines are not quite competitive to today’s best system, but still represent serious baselines for these datasets. Given our setups, fine-tuning with in-domain natural data improves BLEU by almost 4 points for both translation directions on in-domain tests; it also improves, albeit by a smaller margin, the BLEU score of the out-of-domain tests.