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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 15: AAAI-21 Technical Tracks 15

Faster Depth-Adaptive Transformers

February 1, 2023

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Authors

Yijin Liu

Beijing Jiaotong University


Fandong Meng

Tencent WeChat AI - Pattern Recognition Center Tencent Inc.


Jie Zhou

Tencent WeChat AI - Pattern Recognition Center Tencent Inc.


Yufeng Chen

Beijing Jiaotong University


Jinan Xu

Beijing Jiaotong University


DOI:

10.1609/aaai.v35i15.17584


Abstract:

Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency. The main challenge is how to measure such hardness and decide the required depths (i.e., layers) to conduct. Previous works generally build a halting unit to decide whether the computation should continue or stop at each layer. As there is no specific supervision of depth selection, the halting unit may be under-optimized and inaccurate, which results in suboptimal and unstable performance when modeling sentences. In this paper, we get rid of the halting unit and estimate the required depths in advance, which yields a faster depth-adaptive model. Specifically, two approaches are proposed to explicitly measure the hardness of input words and estimate corresponding adaptive depth, namely 1) mutual information (MI) based estimation and 2) reconstruction loss based estimation. We conduct experiments on the text classification task with 24 datasets in various sizes and domains. Results confirm that our approaches can speed up the vanilla Transformer (up to 7x) while preserving high accuracy. Moreover, efficiency and robustness are significantly improved when compared with other depth-adaptive approaches.

Topics: AAAI

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HOW TO CITE:

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu Faster Depth-Adaptive Transformers Proceedings of the AAAI Conference on Artificial Intelligence (2021) 13424-13432.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu Faster Depth-Adaptive Transformers AAAI 2021, 13424-13432.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu (2021). Faster Depth-Adaptive Transformers. Proceedings of the AAAI Conference on Artificial Intelligence, 13424-13432.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu. Faster Depth-Adaptive Transformers. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.13424-13432.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu. 2021. Faster Depth-Adaptive Transformers. "Proceedings of the AAAI Conference on Artificial Intelligence". 13424-13432.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu. (2021) "Faster Depth-Adaptive Transformers", Proceedings of the AAAI Conference on Artificial Intelligence, p.13424-13432

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu, "Faster Depth-Adaptive Transformers", AAAI, p.13424-13432, 2021.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu. "Faster Depth-Adaptive Transformers". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.13424-13432.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu. "Faster Depth-Adaptive Transformers". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 13424-13432.

Yijin Liu||Fandong Meng||Jie Zhou||Yufeng Chen||Jinan Xu. Faster Depth-Adaptive Transformers. AAAI[Internet]. 2021[cited 2023]; 13424-13432.


ISSN: 2374-3468


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