mlsanityData to 7 Oct 2026

Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding

arXiv 1808.07383 ↗First posted 22 Aug 2018cs.LG cs.CL stat.MLcited by 8

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Abstract (arXiv)

In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2018.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 0; 2024: 1; 2025: 1.

8 papers.