方言替代作为一种对抗方法来评估阿拉伯NLP的稳定性
1College of Computer Science and Engineering, Taibah University, Yanbu, 46421, Saudi Arabia. bshemali@taibahu.edu.sa.
Scientific reports
|January 22, 2026
概括
对于阿拉伯自然语言处理 (NLP) 的深度神经网络 (DNN) 易受方言变化的影响. 将方言替代引入现代标准阿拉伯语 (MSA) 文本显著降低了分类准确性,突出了对更强大的模型的需求.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 深度神经网络 (DNN) 已经推进了阿拉伯自然语言处理 (NLP).
- 现有的阿拉伯语NLP系统主要使用现代标准阿拉伯语 (MSA),忽视方言变化.
- 在对抗性环境中DNN的稳定性,特别是关于阿拉伯语的Diglossia,是未被充分探索的.
研究的目的:
- 评估MSA培训数据与现实世界方言阿拉伯语之间的分布不匹配的影响.
- 引入和评估使用方言替代与基于DNN的MSA分类器进行黑子对抗方法.
- 为了揭露阿拉伯语NLP当前系统的漏洞,由于语言的脱口而出.
主要方法:
- 开发了一种使用方言替换的黑盒,代币级对抗性攻击.
- 在输入序列中使用评分函数识别了有影响力的代币.
- 通过精心调整的阿拉伯语方言化器,用方言等价的形式取代了MSA代币.
主要成果:
- 方言替换显著降低了基于DNN的MSA系统的分类准确性.
- 证明了在MSA体上训练的模型对方言变化的脆弱性.
- 量化了由对抗方言转移引起的性能退化.
结论:
- 当前的阿拉伯语NLP系统对阿拉伯语内在的翻译表现出了显著的脆弱性.
- 经过MSA训练的DNN在面对方言文本时难以保持稳定性.
- 未来的研究应该专注于开发更强大,更普遍的阿拉伯语NLP模型,以适应不同的方言.
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