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arXiv · 1912.00544

Multi-Scale Self-Attention for Text Classification

Qipeng Guo·Xipeng Qiu·Pengfei Liu·Xiangyang Xue·Zheng Zhang
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Abstract

In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules. We propose a Multi-Scale Transformer which uses multi-scale multi-head self-attention to capture features from different scales. Based on the linguistic perspective and the analysis of pre-trained Transformer (BERT) on a huge corpus, we further design a strategy to control the scale distribution for each layer. Results of three different kinds of tasks (21 datasets) show our Multi-Scale Transformer outperforms the standard Transformer consistently and significantly on small and moderate size datasets.

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BibTeXRIS

Qipeng Guo, Xipeng Qiu, Pengfei Liu, Xiangyang Xue, Zheng Zhang. 2019-12-02. Multi-Scale Self-Attention for Text Classification. https://arxiv.org/abs/1912.00544

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