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

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arxiv 2001.04063 v3 pith:HIEA3W43 submitted 2020-01-13 cs.CL

Weizhen Qi , Yu Yan , Yeyun Gong , Dayiheng Liu , Nan Duan , Jiusheng Chen , Ruofei Zhang , Ming Zhou This is my paper
classification cs.CL
keywords prophetnetfuturepredictionmodeln-grampre-trainingsequence-to-sequencetokens
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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.

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