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

What can pre-trained multilingual sequenceto-sequence models like mBART contribute to translating low-resource languages? We conduct a thorough empirical experiment in 10 languages to ascertain this, considering five factors: (1) the amount of fine-tuning data, (2) the noise in the fine-tuning data, (3) the amount of pre-training data in the model, (4) the impact of domain mismatch, and (5) language typology. In addition to yielding several heuristics, the experiments form a framework for evaluating the data sensitivities of machine translation systems. While mBART is robust to domain differences, its translations for unseen and typologically distant languages remain below 3.0 BLEU. In answer to our title's question, mBART is not a low-resource panacea; we therefore encourage shifting the emphasis from new models to new data 1 . Dataset Domain Languages FLORES-101 Open all except SI FLORESv1 Open SI CCAligned Open all except GA CCMatrix Open GA JHU Bibles Religious all JW300 Religious+magazines AF, YO, XH