FAI: Quantifying and Mitigating Disparities in Language Technologies

自然语言处理(NLP)技术的进步现在使通过自然语言或通过自然语言数据执行许多任务成为可能--自动系统可以回答问题、执行网络搜索或命令我们的计算机执行特定任务。然而，“语言”并不是一成不变的；人们所说的语言、所使用的方言、产生语言的相对容易程度，或者他们选择用来表达自己的词语，都是不同的。然而，在NLP系统的基准测试中，这种语言变化通常是未经证实的。最常见的任务是使用规范的美式英语来制定的，设计时几乎不考虑系统是否适用于任何其他种类的语言。在这项工作中，我们问了一个简单的问题：我们能否衡量我们使用的语言的多样性在多大程度上影响了我们可以从语言技术系统中预期的结果的质量？这将允许开发和部署与语言技术有关的各种任务的公平准确性衡量标准，鼓励这些技术的最新进展集中在所有方面，而不仅仅是少数几个。具体地说，这项工作集中在这个整体研究问题的四个方面。首先，我们将开发一种通用的方法，用于量化特定的语言技术在许多语言变体中的工作情况。对多个说话者或人口统计数据的测量结合到基准中，这些基准可以推动语言系统公平指标的发展，这些指标是根据设计团队的特定需求量身定做的。其次，我们将超越简单的精度衡量标准，直接根据使用系统得出的相对效用来量化系统精度对用户的影响。这些效用的衡量标准将纳入我们的系统成功衡量标准。第三，我们关注来自不同人口统计群体的人所产生的语言，根据人口统计预测系统的准确性。最后，我们将研究跨越语言或方言边界的NLP系统稳健学习的新方法，并检查这些方法在提高所有用户的准确性方面的效果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Advances in natural language processing (NLP) technology now make it possible to perform many tasks through natural language or over natural language data -- automatic systems can answer questions, perform web search, or command our computers to perform specific tasks. However, ``language'' is not monolithic; people vary in the language they speak, the dialect they use, the relative ease with which they produce language, or the words they choose with which to express themselves. In benchmarking of NLP systems however, this linguistic variety is generally unattested. Most commonly tasks are formulated using canonical American English, designed with little regard for whether systems will work on language of any other variety. In this work we ask a simple question: can we measure the extent to which the diversity of language that we use affects the quality of results that we can expect from language technology systems? This will allow for the development and deployment of fair accuracy measures for a variety of tasks regarding language technology, encouraging advances in the state of the art in these technologies to focus on all, not just a select few.Specifically, this work focuses on four aspects of this overall research question. First, we will develop a general-purpose methodology for quantifying how well particular language technologies work across many varieties of language. Measures over multiple speakers or demographics are combined to benchmarks that can drive progress in development of fair metrics for language systems, tailored to the specific needs of design teams. Second, we will move beyond simple accuracy measures, and directly quantify the effect that the accuracy of systems has on users in terms of relative utility derived from using the system. These measures of utility will be incorporated in our metrics for system success. Third, we focus on the language produced by people from varying demographic groups, predicting system accuracies from demographics. Finally, we will examine novel methods for robust learning of NLP systems across language or dialectal boundaries, and examine the effect that these methods have on increasing accuracy for all users.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.