Papers › Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding
Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding
Deunsol Yoon, Dongbok Lee, SangKeun Lee
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | 2400D Multiple-Dynamic Self-Attention Model | % Test Accuracy | 87.4 | #45 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 2400D Multiple-Dynamic Self-Attention Model | % Train Accuracy | 89.0 | #45 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 2400D Multiple-Dynamic Self-Attention Model | Parameters | 7.0m | #45 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D Dynamic Self-Attention Model | % Test Accuracy | 86.8 | #50 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D Dynamic Self-Attention Model | % Train Accuracy | 87.3 | #50 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D Dynamic Self-Attention Model | Parameters | 2.1m | #50 of 98 | Archive leaderboard | report |
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