Finding the Winning Ticket of BERT for Binary Text Classification via Adaptive Layer Truncation before Fine-tuning

• URL: http://arxiv.org/abs/2111.10951v1
• Date: Mon, 22 Nov 2021 02:22:47 GMT
• Title: Finding the Winning Ticket of BERT for Binary Text Classification via Adaptive Layer Truncation before Fine-tuning
• Authors: Jing Fan, Xin Zhang, Sheng Zhang
• Abstract summary: We construct a series of BERT-based models with different size and compare their predictions on 8 binary classification tasks. The results show there truly exist smaller sub-networks performing better than the full model.
• Score: 7.797987384189306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations. We construct a series of BERT-based models with different size and compare their predictions on 8 binary classification tasks. The results show there truly exist smaller sub-networks performing better than the full model. Then we present a further study and propose a simple method to shrink BERT appropriately before fine-tuning. Some extended experiments indicate that our method could save time and storage overhead extraordinarily with little even no accuracy loss.

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