论文标题
先知网络：预测序列到序列预训练的未来n-gram
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
论文作者
论文摘要
本文介绍了一个新的序列前训练模型，称为Prophetnet，该模型介绍了一个新型的自我监督目标，名为Future N-Gram预测和提出的N-Stream自我发项机制。 Prophetnet不是在传统的序列到序列模型中优化一步预测的，而是通过N-Step前进预测优化了先知，该预测在每个时间步骤中基于先前的上下文标记同时预测下一个N标记。未来的n-gram预测明确鼓励该模型为未来的代币计划，并防止过度拟合强大的局部相关性。我们分别使用基本比例数据集（16GB）和大规模数据集（160GB）预先培训ProPHETNET。然后，我们对CNN/Dailymail，Gigaword和Squad 1.1进行抽象性摘要和问题生成任务的基准进行实验。实验结果表明，与使用相同规模的训练前语料库相比，Prophetnet在所有这些数据集上实现了所有这些数据集的最新结果。
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.
