What can pre-trained multilingual sequence-to-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.

通过在 10 种语言上进行完备实验，考虑了五个因素：（1）微调数据量，（2）微调数据中的噪声，（3）模型预训练数据量，（4）领域不匹配的影响以及（5）语言类型学，我们试图确定预训练多语言序列到序列模型（如 mBART）对翻译低资源语言有何贡献，并提出了许多启发式方法。实验结果显示，mBART 对领域差异具有鲁棒性，但对未知和语言类型学差异较大的语言的翻译仍低于 3.0 BLEU。因此，我们鼓励将重点从新模型转向新数据。