Fengyu Wang
2022
bert2BERT: Towards Reusable Pretrained Language Models
Cheng Chen
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Yichun Yin
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Lifeng Shang
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Xin Jiang
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Yujia Qin
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Fengyu Wang
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Zhi Wang
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Xiao Chen
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Zhiyuan Liu
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Qun Liu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
In recent years, researchers tend to pre-train ever-larger language models to explore the upper limit of deep models. However, large language model pre-training costs intensive computational resources, and most of the models are trained from scratch without reusing the existing pre-trained models, which is wasteful. In this paper, we propose bert2BERT, which can effectively transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and significantly improve the pre-training efficiency of the large model. Specifically, we extend the previous function-preserving method proposed in computer vision on the Transformer-based language model, and further improve it by proposing a novel method, advanced knowledge for large model’s initialization. In addition, a two-stage learning method is proposed to further accelerate the pre-training. We conduct extensive experiments on representative PLMs (e.g., BERT and GPT) and demonstrate that (1) our method can save a significant amount of training cost compared with baselines including learning from scratch, StackBERT and MSLT; (2) our method is generic and applicable to different types of pre-trained models. In particular, bert2BERT saves about 45% and 47% computational cost of pre-training BERT\rm BASE and GPT\rm BASE by reusing the models of almost their half sizes.
2021
Automatic Construction of Sememe Knowledge Bases via Dictionaries
Fanchao Qi
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Yangyi Chen
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Fengyu Wang
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Zhiyuan Liu
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Xiao Chen
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Maosong Sun
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
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Co-authors
- Xiao Chen 2
- Zhiyuan Liu 2
- Cheng Chen 1
- Yichun Yin 1
- Lifeng Shang 1
- show all...