Online Learning for Effort Reduction in Interactive Neural Machine Translation
Table 7: Vocabulary coverage (C), restricted repetition rate (RRR) and unseen n-gram fraction (UNF) with respect to the out-of-domain corpus (Europarl). We applied the BPE codification learned for the Europarl corpus to each document.
['[EMPTY]', '[EMPTY]', 'Training  [ITALIC] C', 'Development  [ITALIC] C', 'Test  [ITALIC] C', 'Test RRR\xa0[%]', 'Test UNF\xa0[%]']
We took as general system the one trained with the Europarl corpus. In order to work with the same vocabulary, we applied the same BPE segmentation to all in-domain sets. All values were computed according to the BPE version of each test document. As expected, the development of adaptive NMT systems greatly improved the quality of the systems. The improvements brought by continuous learning to the XRCE task were large (17.9/17.4 points in terms of TER and 13.8/16.3 in terms of BLEU). These large improvements were due to the aforementioned structure features of this text. Again, we used the Europarl corpus as out-of-domain. Only in the UFAL corpus the coverage was slightly lower (from 85.9% to 92.5%). The addition of OL to the NMT systems generally improved the performance with respect to static systems. In the XRCE and UFAL tasks, the improvements were large: From 3.2 to 6.9 TER points. The TED task was also benefited from OL, but to a lower extent. Even though, we observed significant improvements in the En→De.