Word embeddings are usually derived from corpora containing text from many
individuals, thus leading to general purpose representations rather than
individually personalized representations. While personalized embeddings can be
useful to improve language model performance and other language processing
tasks, they can only be computed for people with a large amount of longitudinal
data, which is not the case for new users. We propose a new form of
personalized word embeddings that use demographic-specific word representations
derived compositionally from full or partial demographic information for a user
(i.e., gender, age, location, religion). We show that the resulting
demographic-aware word representations outperform generic word representations
on two tasks for English: language modeling and word associations. We further
explore the trade-off between the number of available attributes and their
relative effectiveness and discuss the ethical implications of using them.

研究提出了一种新的形式的个性化词嵌入，使用基于用户的完整或部分人口统计信息的构成导出，展示了人口统计学感知的词表示在两个英语任务（语言建模和词语关联）上优于通用词表示，并探讨了可用属性的数量和它们的相对有效性之间的权衡，并讨论了使用它们的伦理影响。