Universal Neural Machine Translation for Extremely Low Resource Languages
Table 3: BLEU scores evaluated on test set (6k), compared with ULR and MoLE. “vanilla" is the standard NMT system trained only on Ro-En training set
['Models', 'BLEU']
Using Multi-NMT with the assistance of 7.8M auxiliary language sentence pairs, Ro-En translation performance gets a substantial improvement which, however, is still limited to be usable. By contrast, the proposed ULR boosts the Multi-NMT significantly with +5.07 BLEU, which is further boosted to +7.98 BLEU when incorporating sentence-level information using both MoLE and BT. Furthermore, it is also shown that ULR works better when a trainable transformation matrix A is used (4th vs 5th row in the table). Note that, although still 5∼6 BLEU scores lower than the full data (×100 large) model.