ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou · 2020
This paper presents a new sequence-tosequence pre-training model called Prophet-Net, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.Instead of optimizing one-stepahead prediction in the traditional sequenceto-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 Prophet-Net achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.