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arXiv · 2002.06170

Transformer on a Diet

Chenguang Wang·Zihao Ye·Aston Zhang·Zheng Zhang·Alexander J. Smola
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Abstract

Transformer has been widely used thanks to its ability to capture sequence information in an efficient way. However, recent developments, such as BERT and GPT-2, deliver only heavy architectures with a focus on effectiveness. In this paper, we explore three carefully-designed light Transformer architectures to figure out whether the Transformer with less computations could produce competitive results. Experimental results on language model benchmark datasets hint that such trade-off is promising, and the light Transformer reduces 70% parameters at best, while obtains competitive perplexity compared to standard Transformer. The source code is publicly available.

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Chenguang Wang, Zihao Ye, Aston Zhang, Zheng Zhang, Alexander J. Smola. 2020-02-14. Transformer on a Diet. https://arxiv.org/abs/2002.06170

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