ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training

Understand

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.