  This paper presents a new sequence-to-sequence pre-training model called
ProphetNet, which introduces a novel self-supervised objective named future
n-gram prediction and the proposed n-stream self-attention mechanism. Instead
of optimizing one-step-ahead prediction in the traditional sequence-to-sequence
model, the ProphetNet is optimized by n-step ahead prediction that predicts the
next n tokens simultaneously based on previous context tokens at each time
step. The future n-gram prediction explicitly encourages the model to plan for
the future tokens and prevent overfitting on strong local correlations. We
pre-train ProphetNet using a base scale dataset (16GB) and a large-scale
dataset (160GB), respectively. Then we conduct experiments on CNN/DailyMail,
Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question
generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the
same scale pre-training corpus.
