Is a Prestigious Job the same as a Prestigious Country? A Case Study on Multilingual Sentence Embeddings and E uropean Countries (2023)

Abstract: We study how multilingual sentence representations capture European countries and occupations 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 feature in the embedding is the political distinction between Eastern and Western Europe and the country’s economic strength in terms of GDP. 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 in three out of four studied models. The exception is a small distilled model that exhibits a connection between occupational prestige and country of origin, which is a potential source of nationality-based discrimination. Our findings are consistent across languages.

## introduction language models and pre-trained representations in natural language processing (nlp) are known to manifest biases against groups of people, including negative stereotypes connected to ethnicity or gender @xcite . it has been extensively studied with monolingual models. multilingual models, often used for model transfer between languages, introduce another potential issue: stereotypes of speakers of some languages can be imposed in other languages covered by the model. in this case study, we try to determine the most prominent biases connected to european countries in multilingual sentence representation models. we adopt an unsupervised methodology ( § 2) based on hand-crafted prompt templates and principle component analysis (pca), originally developed to extract moral sentiments from sentence representation @xcite . our exploration encompasses four sentence representation models across 13 languages ( § 3). we find only minor differences between languages in the models. the results ( § 4) show that the strongest dimension in all models correlates with the political and economic distinction between western and eastern europe and the gross domestic product (gdp). prompting specifically for country prestige leads to similar results. when prompted for occupations, the models can distinguish between low and high-prestige jobs. in most cases, the extracted job-prestige dimension only loosely correlates with the country-prestige dimension. this result suggests that the models do not connect individual social prestige with the country of origin. the exception is a small model distilled from multilingual universal sentence encoder @xcite ) that seems to mix these two and thus confirms previous work claiming that distilled models are more prone to biases @xcite . the source code for the experiments is available on github. 1 ## methodology we analyze sentence representation models ( § 2.1) using a generalization of the moral direction framework ( § 2.2). we represent concepts (countries, jobs) using sets of templated sentences ( § 2.3), for which we compute the sentence embeddings. then, we compute the principal components of the embeddings and analyze what factors statistically explain the main principal component ( § 2.4). ## results aggregated results. the results aggregated over language are presented in when prompting the models for countries, the most prominent dimensions almost always separate the countries according to the political east-west axis, consistently across languages. this is further stressed by the high correlation of the country dimension with the country's gdp, which is particularly strong in multilingual mpnet and distilled muse. when we prompt the models specifically for country prestige, the correlation with the country's gdp slightly increases. when we prompt the models for job prestige, they can distinguish high-and low-prestige jobs well (accuracy 85-93%). when we apply the same projection to prompts about countries, in most cases, the ordering of the countries is random. therefore, we conclude that the models do not correlate job prestige and country of origin. the only exception is distilled muse, where the job-prestige dimension applied to countries still correlates with the country's gdp and the east-west axis. this is consistent with previous work showing that distilled student models exhibit more biases than model trained on authentic data @xcite . differences between languages. further, we evaluate how languages differ from each other. in all models, the first pca dimension from the job-prestige prompts separates low-and highprestige jobs almost perfectly. nevertheless, multilingual mpnet and distilled muse show a relatively low correlation of the job dimension across languages (see figure 1 ). finally, we try to explain the correlation between languages by connecting them to countries where the languages are spoken. we measure how the correlation between languages correlates with the geographical distances of (the biggest) countries speaking the language, the difference in their gdp, and the lexical similarity of the languages. the results are presented in for all models except xlm-r-nli, the lexical similarity of the languages is the strongest predictor. xlm-r-nli, with low differences between languages, better correlates with geographical distances. ## related work societal biases of various types in neural nlp models are widely studied, especially focusing on gender and ethnicity. the results of the efforts were already summarized in comprehensive overviews @xcite . nationality bias has also been studied. @xcite show that gpt-2 associates countries of the global south with negative-sentiment adjectives. however, only a few studies focus on biases in how multilingual models treat different lan-guages. @xcite showed that in spanish and greek, mbert prefers syntactic structures prevalent in english. @xcite and @xcite studied differences in moral biases in multilingual language models, concluding there are some differences but no systematic trends. yin et al. (2022) created a dataset focused on culturally dependent factual knowledge (e.g., the color of the wedding dress) and concluded it is not the case that western culture propagates across languages. ## conclusions we showed that all sentence representation models carry a bias that the most prominent feature of european countries is their economic power and affiliation to former geopolitical western and eastern blocks. in the models we studied, this presumed country prestige does not correlate with how the models represent the occupation status of people. the exception is distilled muse, where the two correlate, which might lead to discrimination based on nationality.