Fine-Tuning Self-Supervised Multilingual Sequence-To-Sequence Models for Extremely Low-Resource NMT

Neural Machine Translation (NMT) tends to perform poorly in low-resource language settings due to the scarcity of parallel data. Instead of relying on inadequate parallel corpora, we can take advantage of monolingual data available in abundance. Training a denoising self-supervised multilingual sequence-to-sequence model by noising the available large scale monolingual corpora is one way to utiliz...