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

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We study how multilingual sentence representations capture European countriesand occupations and how this differs across European languages. We prompt themodels with templated sentences that we machine-translate into 12 Europeanlanguages and analyze the most prominent dimensions in the embeddings.Ouranalysis reveals that the most prominent feature in the embedding is thegeopolitical distinction between Eastern and Western Europe and the country'seconomic strength in terms of GDP. When prompted specifically for job prestige,the embedding space clearly distinguishes high and low-prestige jobs. Theoccupational dimension is uncorrelated with the most dominant countrydimensions in three out of four studied models. The exception is a smalldistilled model that exhibits a connection between occupational prestige andcountry of origin, which is a potential source of nationality-baseddiscrimination. Our findings are consistent across languages.

Is a Prestigious Job the same as a Prestigious Country? A Case Study on Multilingual Sentence Embeddings and European Countries