Translationese as a Language in “Multilingual” NMT
Table 7: Average BLEU scores for models trained on (a) WMT 2018 English→French bitext plus 39M back-translated monolingual sentences, and (b) WMT 2018 English→German bitext plus 24M back-translated monolingual sentences. As before, we tag by heuristics and/or classifier predictions on the target (German) side.
['Test set → Decode as if →', 'Test set → Decode as if →', 'Src-Orig Natural', 'Src-Orig Transl.', 'Trg-Orig Transl.', 'Trg-Orig Natural', 'Combined Both']
While combining the bitext classified by the FT classifier with all-tagged BT data yields a minor gain of 0.2 BLEU over the TBT baseline of Caswell et al. , the other methods do not beat the baseline. This indicates that assuming all of the target monolingual data to be original is not as harmful as the error introduced by the classifiers. Combining the bitext classified by the RTT classifier with all-tagged BT data matched the performance of the TBT baseline, but none of the models outperformed it. This is expected, given the poor performance of the bitext-only models for this language pair.