Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation
Table 3: Results on MultiUN test sets. The six zero-shot translation directions are evaluated. The column “A-ZST" reports averaged BLEU of zero-shot translation, while the column “A-ST" reports averaged BLEU of supervised pivot→target direction.
['MultiUN Direction', 'Ar,Es,Ru ↔ En Ar → Es', 'Ar,Es,Ru ↔ En Es → Ar', 'Ar,Es,Ru ↔ En Ar → Ru', 'Ar,Es,Ru ↔ En Ru → Ar', 'Ar,Es,Ru ↔ En Es → Ru', 'Ar,Es,Ru ↔ En Ru → Es', 'Ar,Es,Ru ↔ En A-ZST', 'Ar,Es,Ru ↔ En A-ST']
We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) [johnson2017google], and cross-lingual transfer without pretraining [kim2019effective]. The results show that our approaches consistently outperform other approaches across languages and datasets, especially surpass pivoting, which is a strong baseline in the zero-shot scenario that multilingual NMT systems often fail to beat [johnson2017google, al2019consistency, arivazhagan2019missing]. Pivoting translates source to pivot then to target in two steps, causing inefficient translation process. Our approaches use one encoder-decoder model to translate between any zero-shot directions, which is more efficient than pivoting. Regarding the comparison between transfer approaches, our cross-lingual pretraining based transfer outperforms transfer method that does not use pretraining by a large margin.