Since Labov's (1964) foundational work on the social stratification of
language, linguistics has dedicated concerted efforts towards understanding the
relationships between socio-demographic factors and language production and
perception. Despite the large body of evidence identifying significant
relationships between socio-demographic factors and language production,
relatively few of these factors have been investigated in the context of NLP
technology. While age and gender are well covered, Labov's initial target,
socio-economic class, is largely absent. We survey the existing Natural
Language Processing (NLP) literature and find that only 20 papers even mention
socio-economic status. However, the majority of those papers do not engage with
class beyond collecting information of annotator-demographics. Given this
research lacuna, we provide a definition of class that can be operationalised
by NLP researchers, and argue for including socio-economic class in future
language technologies.

在自然语言处理技术中，虽然已经研究了年龄和性别等方面与语言产生之间的关系，但至今较少研究 社会经济阶级等因素。本研究通过对现有自然语言处理文献进行调查，发现只有 20 篇论文提及社会经济阶级，并且大多数论文仅限于收集注释者人口统计信息，而未深入研究该阶级。鉴于此项研究空白，我们提供了一个可在自然语言处理研究中操作的社会经济阶级定义，并主张在今后的语言技术中应包含社会经济阶级。

贫穷的语言技术：NLP 中的社会（阶级）缺失

Impoverished Language Technology: The Lack of (Social) Class in NLP

Inspired by Labov's seminal work on stylistic variation as a function of
social stratification, we develop and compare neural models that predict a
person's presumed socio-economic status, obtained through distant
supervision,from their writing style on social media. The focus of our work is
on identifying the most important stylistic parameters to predict
socio-economic group. In particular, we show the effectiveness of
morpho-syntactic features as stylistic predictors of socio-economic group,in
contrast to lexical features, which are good predictors of topic.

研究社会阶层的影响因素，使用神经模型预测社交媒体上个人的社会经济地位，探讨语言风格在预测社会经济群体方面的作用，发现新颖的词法 - 句法特征是一种有效的风格预测因素。