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

Sense embedding learning methods learn different embeddings for the differentsenses of an ambiguous word. One sense of an ambiguous word might be sociallybiased while its other senses remain unbiased. In comparison to the numerousprior work evaluating the social biases in pretrained word embeddings, thebiases in sense embeddings have been relatively understudied. We create abenchmark dataset for evaluating the social biases in sense embeddings andpropose novel sense-specific bias evaluation measures. We conduct an extensiveevaluation of multiple static and contextualised sense embeddings for varioustypes of social biases using the proposed measures. Our experimental resultsshow that even in cases where no biases are found at word-level, there stillexist worrying levels of social biases at sense-level, which are often ignoredby the word-level bias evaluation measures.

Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings