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

Creating accurate meta-embeddings from pre-trained source embeddings hasreceived attention lately. Methods based on global and locally-lineartransformation and concatenation have shown to produce accuratemeta-embeddings. In this paper, we show that the arithmetic mean of twodistinct word embedding sets yields a performant meta-embedding that iscomparable or better than more complex meta-embedding learning methods. Theresult seems counter-intuitive given that vector spaces in different sourceembeddings are not comparable and cannot be simply averaged. We give insightinto why averaging can still produce accurate meta-embedding despite theincomparability of the source vector spaces.

Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings