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The Ethereal
Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding
August 22, 2018 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Deunsol Yoon, Dongbok Lee, SangKeun Lee
arXiv ID
1808.07383
Category
cs.LG: Machine Learning
Cross-listed
cs.CL,
stat.ML
Citations
44
Venue
arXiv.org
Last Checked
6 months ago
Abstract
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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