Meta-Learning for Low-Resource Neural Machine Translation
Table 2: BLEU Scores w.r.t. the source task set for all five target tasks.
['Meta-Train', 'Ro-En zero', 'Ro-En finetune', 'Lv-En zero', 'Lv-En finetune', 'Fi-En zero', 'Fi-En finetune', 'Tr-En zero', 'Tr-En finetune', 'Ko-En zero', 'Ko-En finetune']
, we present the results on all five target tasks obtained while varying the source task set. We first see that it is always beneficial to use more source tasks. Although the impact of adding more source tasks varies from one language to another, there is up to 2× improvement going from one source task to 18 source tasks (Lv-En, Fi-En , Tr-En and Ko-En). In addition, the choice of source languages has different implications for different target languages. For instance, Ro-En benefits more from {Es, Fr, It, Pt} than from {De, Ru}, while the opposite effect is observed with all the other target tasks.