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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 8: AAAI-21 Technical Tracks 8

Learning to Augment for Data-scarce Domain BERT Knowledge Distillation

February 1, 2023

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Authors

Lingyun Feng

Tsinghua University


Minghui Qiu

Alibaba Group


Yaliang Li

Alibaba Group


Hai-Tao Zheng

Tsinghua University


Ying Shen

Sun Yat-Sen University


DOI:

10.1609/aaai.v35i8.16910


Abstract:

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of Natural Language Processing (NLP) tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment data for BERT Knowledge Distillation in target domains with scarce labeled data, by learning a cross-domain manipulation scheme that automatically augments the target domain with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced controller to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on different NLP tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ~13.3%) when only a few labeled examples available.

Topics: AAAI

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HOW TO CITE:

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen Learning to Augment for Data-scarce Domain BERT Knowledge Distillation Proceedings of the AAAI Conference on Artificial Intelligence (2021) 7422-7430.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen Learning to Augment for Data-scarce Domain BERT Knowledge Distillation AAAI 2021, 7422-7430.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen (2021). Learning to Augment for Data-scarce Domain BERT Knowledge Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 7422-7430.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen. Learning to Augment for Data-scarce Domain BERT Knowledge Distillation. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.7422-7430.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen. 2021. Learning to Augment for Data-scarce Domain BERT Knowledge Distillation. "Proceedings of the AAAI Conference on Artificial Intelligence". 7422-7430.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen. (2021) "Learning to Augment for Data-scarce Domain BERT Knowledge Distillation", Proceedings of the AAAI Conference on Artificial Intelligence, p.7422-7430

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen, "Learning to Augment for Data-scarce Domain BERT Knowledge Distillation", AAAI, p.7422-7430, 2021.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen. "Learning to Augment for Data-scarce Domain BERT Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.7422-7430.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen. "Learning to Augment for Data-scarce Domain BERT Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 7422-7430.

Lingyun Feng||Minghui Qiu||Yaliang Li||Hai-Tao Zheng||Ying Shen. Learning to Augment for Data-scarce Domain BERT Knowledge Distillation. AAAI[Internet]. 2021[cited 2023]; 7422-7430.


ISSN: 2374-3468


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