mlsanityData to 7 Oct 2026

Multi-Scale Self-Attention for Text Classification

arXiv 1912.00544 ↗First posted 2 Dec 2019cs.CL cs.LGcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2019.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 2; 2024: 1; 2025: 1; 2026: 1.

15 papers.

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