Adapting Multilingual Neural Machine Translation to Unseen Languages
Table 2: BLEU scores for the four LRL→en comparing against previous approaches; RapAdapt [21], SDE [32], Many↔Many [1], and Data-Augment [34]. Bi is an adaptation with the LRL + [closest-HRL] according to Select-one strategy.
['[EMPTY]', 'Strategy', 'az[tr]', 'be[ru]', 'gl[pt]', 'sk[cs]', 'AVG.']
(RapAdapt) and the best performing adaptation approach (SDE), against the Δ between our baseline and our best performing approach (DynAdapt). Even with stronger baselines, our Δ is higher than in the previous approaches with +2.77 BLEU averaged over the four test languages. Note that the MNMT model refers to a training setting with all except for the test language (cold start). We also note that our DirAdapt outperformed the RapAdapt and SDE with a larger margin in all the test languages.