MIT Press

Table 5:

Generalization to unseen languages Language transfer results, fine-tuning on language-pairs without pre-training on them. mBART25 uses all languages during pre-training, while other settings contain at least one unseen language pair. For each model, we also show the gap to mBART25 results.

	Monolingual	Nl-En	En-Nl	Ar-En	En-Ar	Nl-De	De-Nl
Random	None	34.6 (−8.7)	29.3 (−5.5)	27.5 (−10.1)	16.9 (−4.7)	21.3 (−6.4)	20.9 (−5.2)
mBART02	En Ro	41.4 (−2.9)	34.5 (−0.3)	34.9 (−2.7)	21.2 (−0.4)	26.1 (−1.6)	25.4 (−0.7)
mBART06	En Ro Cs It Fr Es	43.1 (−0.2)	34.6 (−0.2)	37.3 (−0.3)	21.1 (−0.5)	26.4 (−1.3)	25.3 (−0.8)
mBART25	All	43.3	34.8	37.6	21.6	27.7	26.1

	Monolingual	Nl-En	En-Nl	Ar-En	En-Ar	Nl-De	De-Nl
Random	None	34.6 (−8.7)	29.3 (−5.5)	27.5 (−10.1)	16.9 (−4.7)	21.3 (−6.4)	20.9 (−5.2)
mBART02	En Ro	41.4 (−2.9)	34.5 (−0.3)	34.9 (−2.7)	21.2 (−0.4)	26.1 (−1.6)	25.4 (−0.7)
mBART06	En Ro Cs It Fr Es	43.1 (−0.2)	34.6 (−0.2)	37.3 (−0.3)	21.1 (−0.5)	26.4 (−1.3)	25.3 (−0.8)
mBART25	All	43.3	34.8	37.6	21.6	27.7	26.1