We study how multilingual sentence representations capture European countries
and how this differs across European languages. We prompt the models with
templated sentences that we machine-translate into 12 European languages and
analyze the most prominent dimensions in the embeddings. Our analysis reveals
that the most prominent country feature in the embedding is its economic
strength in terms of GPD. When prompted specifically for job prestige, the
embedding space clearly distinguishes high and low-prestige jobs. The
occupational dimension is uncorrelated with the most dominant country
dimensions for three out of four studied models. One model: Distilled
Multilingual Universal Sentence Encoder, however, exhibited a connection
between occupational prestige and country of origin, which is a potential
source of nationality-based discrimination. Our findings are consistent across
languages and, to some extent, with the exception mentioned above, across
studied representation models.

研究多语言句子表示如何捕捉欧洲国家以及这在欧洲语言中的差异，发现嵌入中最突出的国家特征是其以 GPD 为代表的经济实力。同时，职业维度与国家维度不相关，但某些模型存在职业声望与原籍国之间的联系，这可能是基于国籍的歧视的潜在来源。在所研究的表示模型中，这些发现在语言上是一致的，但在某些方面除外。