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

A notable property of word embeddings is that word relationships can exist aslinear substructures in the embedding space. For example, $\textit{gender}$corresponds to $\vec{\textit{woman}} - \vec{\textit{man}}$ and$\vec{\textit{queen}} - \vec{\textit{king}}$. This, in turn, allows wordanalogies to be solved arithmetically: $\vec{\textit{king}} -\vec{\textit{man}} + \vec{\textit{woman}} \approx \vec{\textit{queen}}$. Thisproperty is notable because it suggests that models trained on word embeddingscan easily learn such relationships as geometric translations. However, thereis no evidence that models $\textit{exclusively}$ represent relationships inthis manner. We document an alternative way in which downstream models mightlearn these relationships: orthogonal and linear transformations. For example,given a translation vector for $\textit{gender}$, we can find an orthogonalmatrix $R$, representing a rotation and reflection, such that$R(\vec{\textit{king}}) \approx \vec{\textit{queen}}$ and$R(\vec{\textit{man}}) \approx \vec{\textit{woman}}$. Analogical reasoningusing orthogonal transformations is almost as accurate as using vectorarithmetic; using linear transformations is more accurate than both. Ourfindings suggest that these transformations can be as good a representation ofword relationships as translation vectors.

Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space