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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.217.pdf
- Data
- BookCorpus, C4, CNN/Daily Mail, SQuAD