Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?
This addresses the problem of low-resource language translation for NLP researchers and practitioners, but it is incremental as it evaluates an existing model.
The paper investigated whether pre-trained multilingual sequence-to-sequence models like mBART can effectively translate low-resource languages, finding that translations for unseen and typologically distant languages remain below 3.0 BLEU, indicating it is not a panacea.
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.