攻击性语言检测的跨域泛化成本
The Cross-Domain Generalization Cost of Offensive Language Detection
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中文总结 AI 辅助
研究攻击性语言检测模型跨域泛化性能下降问题，提出含零样本转移损失分解、可控微调协议和联合训练策略的框架，量化各因素影响，揭示数据集效应主导损失，联合训练策略可权衡多语言能力与源任务性能。
中文摘要 AI 辅助
攻击性语言检测模型在跨数据集和跨语言部署时通常性能下降，现有研究多仅报告此现象，缺乏系统方法分解降级原因及量化修复成本。本文提出由三个协同技术组件组成的诊断与优化框架。一是零样本转移损失分解，分离性能下降的数据集和语言效应；二是可控微调协议，量化适应效率和对源任务的潜在损害；三是三种联合训练策略，在提高多语言能力和保留源任务性能间提供可控权衡。实验表明数据集效应主导零样本转移损失且远超语言效应，无重放机制的少样本适应对源任务损害大且不稳定，联合训练策略形成清晰可控的帕累托权衡。
英文摘要
Offensive language detection models generally suffer performance degradation when deployed across datasets and across languages, yet most existing studies stop at reporting this phenomenon and lack a systematic methodology for decomposing the causes of degradation into attributable components and quantifying the cost of remediation. This paper proposes a diagnosis and optimization framework composed of three coordinated technical components. First, a zero-shot transfer loss decomposition that separates the performance degradation from OLID to MLMA into two independently measurable components, namely dataset effect and language effect. Second, a controlled fine-tuning protocol that quantifies both adaptation efficiency and the hidden damage inflicted on the source task by comparing few shot learning curves under continued fine-tuning and cold-start starting points. Third, three joint training strategies incorpo rating temperature sampling and experience replay, which offer a controllable Pareto trade-off between improving multilingual capability and preserving source-task performance. Experiments built on this framework show that the dataset effect dominates the zero-shot transfer loss and substantially outweighs the language effect. Few-shot adaptation without a replay mechanism, though data-efficient, inflicts source task damage 4 to 9 times greater than that of the joint training strategies, and its damage magnitude is highly unstable. The three joint training strategies trade 3.2 to 4.1 percentage points of source-task performance for 8.1 to 42.6 percentage points of multilingual capability gain, forming a clear and controllable Pareto trade-off.
发表机构
- School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子信息工程学院）
- National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC)（国家计算机网络应急技术处理协调中心）
机构由 AI 辅助整理，请以论文原文为准。
