文化罗盘：利用文化特征预测冒犯性语言检测中迁移学习的成功
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features
AI总结：
本文研究文化特征对冒犯性语言检测跨文化迁移学习成功的预测作用，发现文化价值观调查结合冒犯性词语距离可有效预测迁移效果，并建议在数据集中融入文化信息以提升语言技术的文化适应性。
摘要：
语言技术的日益普及要求在机器学习领域转向考虑文化多样性，尤其是对于依赖文化细微差别的主观任务，如冒犯性语言检测（OLD）。当前的理解强调这些任务在很大程度上受到文化价值观的影响，然而，在确定文化特征能否准确预测此类主观任务的跨文化迁移学习成功方面，仍存在明显空白。针对这一问题，我们的研究深入探讨了文化特征与迁移学习有效性之间的交叉点。研究结果表明，文化价值观调查确实对OLD任务中跨文化迁移学习的成功具有预测能力，并且通过使用冒犯性词语距离可以进一步提高这种预测能力。基于这些结果，我们主张将文化信息整合到数据集中。此外，我们建议利用富含文化信息的数据源（如调查）来增强文化适应性。我们的研究标志着在追求更具包容性、文化敏感的语言技术方面向前迈进了一步。
英文摘要：
The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, however, a notable gap exists in determining if cultural features can accurately predict the success of cross-cultural transfer learning for such subjective tasks. Addressing this, our study delves into the intersection of cultural features and transfer learning effectiveness. The findings reveal that cultural value surveys indeed possess a predictive power for cross-cultural transfer learning success in OLD tasks and that it can be further improved using offensive word distance. Based on these results, we advocate for the integration of cultural information into datasets. Additionally, we recommend leveraging data sources rich in cultural information, such as surveys, to enhance cultural adaptability. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.
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。
