English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose Dynamic Self-Attention (DSA), a new self-attentionmechanism for sentence embedding. We design DSA by modifying dynamic routing incapsule network (Sabouretal.,2017) for natural language processing. DSA attendsto informative words with a dynamic weight vector. We achieve newstate-of-the-art results among sentence encoding methods in Stanford NaturalLanguage Inference (SNLI) dataset with the least number of parameters, whileshowing comparative results in Stanford Sentiment Treebank (SST) dataset.

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