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

Abstract
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.
Anthology ID:
2020.findings-emnlp.217
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2401–2410
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.217
DOI:
10.18653/v1/2020.findings-emnlp.217
Bibkey:
Cite (ACL):
Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020. ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2401–2410, Online. Association for Computational Linguistics.
Cite (Informal):
ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training (Qi et al., Findings 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2020.findings-emnlp.217.pdf
Data
BookCorpus, C4, CNN/Daily Mail, SQuAD
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