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What can pre-trained multilingual sequence-to-sequence models like mBARTcontribute to translating low-resource languages? We conduct a thoroughempirical experiment in 10 languages to ascertain this, considering fivefactors: (1) the amount of fine-tuning data, (2) the noise in the fine-tuningdata, (3) the amount of pre-training data in the model, (4) the impact ofdomain mismatch, and (5) language typology. In addition to yielding severalheuristics, the experiments form a framework for evaluating the datasensitivities of machine translation systems. While mBART is robust to domaindifferences, its translations for unseen and typologically distant languagesremain below 3.0 BLEU. In answer to our title's question, mBART is not alow-resource panacea; we therefore encourage shifting the emphasis from newmodels to new data.

Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?