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Multi-Scale Self-Attention for Text Classification

2019-12-02 02:08:00
Qipeng Guo, Xipeng Qiu, Pengfei Liu, Xiangyang Xue, Zheng Zhang
arXiv_CL
               
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.

Abstract (translated)

URL

https://arxiv.org/abs/1912.00544

PDF

https://arxiv.org/pdf/1912.00544.pdf

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