  Pre-training and fine-tuning, e.g., BERT, have achieved great success in
language understanding by transferring knowledge from rich-resource
pre-training task to the low/zero-resource downstream tasks. Inspired by the
success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for
the encoder-decoder based language generation tasks. MASS adopts the
encoder-decoder framework to reconstruct a sentence fragment given the
remaining part of the sentence: its encoder takes a sentence with randomly
masked fragment (several consecutive tokens) as input, and its decoder tries to
predict this masked fragment. In this way, MASS can jointly train the encoder
and decoder to develop the capability of representation extraction and language
modeling. By further fine-tuning on a variety of zero/low-resource language
generation tasks, including neural machine translation, text summarization and
conversational response generation (3 tasks and totally 8 datasets), MASS
achieves significant improvements over the baselines without pre-training or
with other pre-training methods. Specially, we achieve the state-of-the-art
accuracy (37.5 in terms of BLEU score) on the unsupervised English-French
translation, even beating the early attention-based supervised model.
