ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
This addresses the issue of overfitting in sequence generation for NLP tasks like summarization and question generation, offering incremental improvements over existing pre-training methods.
The paper tackles the problem of improving sequence-to-sequence models by introducing ProphetNet, which uses future n-gram prediction to plan ahead for multiple tokens, achieving new state-of-the-art results on benchmarks like CNN/DailyMail, Gigaword, and SQuAD 1.1.
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.