Pre-trained multilingual language models (PMLMs) are commonly used when
dealing with data from multiple languages and cross-lingual transfer. However,
PMLMs are trained on varying amounts of data for each language. In practice
this means their performance is often much better on English than many other
languages. We explore to what extent this also applies to moral norms. Do the
models capture moral norms from English and impose them on other languages? Do
the models exhibit random and thus potentially harmful beliefs in certain
languages? Both these issues could negatively impact cross-lingual transfer and
potentially lead to harmful outcomes. In this paper, we (1) apply the
MoralDirection framework to multilingual models, comparing results in German,
Czech, Arabic, Mandarin Chinese, and English, (2) analyse model behaviour on
filtered parallel subtitles corpora, and (3) apply the models to a Moral
Foundations Questionnaire, comparing with human responses from different
countries. Our experiments demonstrate that, indeed, PMLMs encode differing
moral biases, but these do not necessarily correspond to cultural differences
or commonalities in human opinions.

本文探讨了预训练的多语言语言模型是否会从英语中捕捉道德规范，并将其强加在其他语言中，以及它们是否在某些语言中表现出随机且潜在有害的信念。研究还在多语言模型上应用了 MoralDirection 框架，分析了在过滤的平行字幕语料库上的模型行为，并将模型应用于道德基础调查问卷，比较不同国家的人类反应。实验表明，预训练的多语言语言模型确实会编码不同的道德偏见，但这些偏见不一定对应于人类观点的文化差异或共性。