通过多任务学习解耦方言与社会偏见以提升公平性
Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
AI总结：
针对偏见语言检测中方言导致的性能差异，本文提出将方言建模作为辅助任务的多任务学习方法，在非裔美式英语实验中提升公平性并取得SOTA效果。
摘要：
方言在地区或社会群体使用的语言中引入了句法和词汇差异。大多数NLP方法对此类差异并不敏感。这可能导致这些方法表现出不公平行为，向方言使用者传递负面偏见。尽管以往工作已经研究了仇恨言论等方面的方言相关公平性，但偏见语言的其他方面（例如猥亵性）仍完全未被探索。为填补这一空白，我们研究了在检测偏见语言的五个方面时不同方言之间的性能差异，以及如何缓解这些差异。为减轻偏见，我们提出一种多任务学习方法，将方言语言建模为辅助任务，以纳入句法和词汇差异。在针对非裔美式英语方言的实验中，我们提供了实证证据，表明用方言建模补充常见学习方法可提升其公平性。此外，结果表明，多任务学习达到了最先进性能，并有助于更可靠地检测偏见语言的属性。
英文摘要：
Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
